Machine Learning for Drug Discovery.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning for Drug Discovery"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The time taken for a drug to jump past all the hurdles from initial discovery to being marketed can be roughly a decade and the cost associated with research and subsequent clinical trials is in the order of a billion US dollars.[¹](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3058157/) This is not the end of the list of challenges as only 12% of of drugs make it to market.[²](https://www.cbo.gov/publication/57126). The aim of this project is to train a model that can **predict IC50 values**, which is a measure of drug potency based on **PubChem fingerprints**. PubChem fingerprints are a series of binary digits used to represent the chemical structure. For example, the first bit position indicates whether the compound has at least 4 hydrogen atoms.[³](https://web.cse.ohio-state.edu/~zhang.10631/bak/drugreposition/list_fingerprints.pdf) With a well trained model, we can then look at the **important features for IC50 prediction** to understand which structural features drive potency to aid optimisation of the chemical structure. This model would also be helpful for predicting IC50 values of libraries of compounds against the target protein, aromotase, which can be useful to narrow down compounds to test in the lab.\n",
    "\n",
    "This project is inspired by a bioinformatics project done by Chanin Nantasenamat.[⁴](https://github.com/dataprofessor/code/tree/master/python)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data collection and pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Building wheels for collected packages: easydict\n",
      "  Building wheel for easydict (setup.py): started\n",
      "  Building wheel for easydict (setup.py): finished with status 'done'\n",
      "  Created wheel for easydict: filename=easydict-1.10-py3-none-any.whl size=6492 sha256=3b2bbb87a0bef69d3fe09ae0de88664d5b275d838b6476f2059783be2c74a124\n",
      "  Stored in directory: c:\\users\\chuaw\\appdata\\local\\pip\\cache\\wheels\\8d\\d6\\16\\3fd964549d5c27d89d6c5fdb4306283ca55be1799f3d48a67b\n",
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      "Installing collected packages: easydict, urllib3, url-normalize, pyyaml, itsdangerous, idna, charset-normalizer, requests, requests-cache, chembl_webresource_client\n",
      "Successfully installed charset-normalizer-2.1.1 chembl_webresource_client-0.10.8 easydict-1.10 idna-3.4 itsdangerous-2.1.2 pyyaml-6.0 requests-2.28.1 requests-cache-0.7.5 url-normalize-1.4.3 urllib3-1.26.12\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install chembl_webresource_client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing necessary libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set(style='ticks')\n",
    "import matplotlib.pyplot as plt\n",
    "from chembl_webresource_client.new_client import new_client\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import Descriptors,Lipinski,rdchem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cross_references</th>\n",
       "      <th>organism</th>\n",
       "      <th>pref_name</th>\n",
       "      <th>score</th>\n",
       "      <th>species_group_flag</th>\n",
       "      <th>target_chembl_id</th>\n",
       "      <th>target_components</th>\n",
       "      <th>target_type</th>\n",
       "      <th>tax_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[{'xref_id': 'P11511', 'xref_name': None, 'xre...</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>Cytochrome P450 19A1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>False</td>\n",
       "      <td>CHEMBL1978</td>\n",
       "      <td>[{'accession': 'P11511', 'component_descriptio...</td>\n",
       "      <td>SINGLE PROTEIN</td>\n",
       "      <td>9606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[{'xref_id': 'P22443', 'xref_name': None, 'xre...</td>\n",
       "      <td>Rattus norvegicus</td>\n",
       "      <td>Cytochrome P450 19A1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>False</td>\n",
       "      <td>CHEMBL3859</td>\n",
       "      <td>[{'accession': 'P22443', 'component_descriptio...</td>\n",
       "      <td>SINGLE PROTEIN</td>\n",
       "      <td>10116</td>\n",
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      "text/plain": [
       "                                    cross_references           organism  \\\n",
       "0  [{'xref_id': 'P11511', 'xref_name': None, 'xre...       Homo sapiens   \n",
       "1  [{'xref_id': 'P22443', 'xref_name': None, 'xre...  Rattus norvegicus   \n",
       "\n",
       "              pref_name  score  species_group_flag target_chembl_id  \\\n",
       "0  Cytochrome P450 19A1   20.0               False       CHEMBL1978   \n",
       "1  Cytochrome P450 19A1   20.0               False       CHEMBL3859   \n",
       "\n",
       "                                   target_components     target_type  tax_id  \n",
       "0  [{'accession': 'P11511', 'component_descriptio...  SINGLE PROTEIN    9606  \n",
       "1  [{'accession': 'P22443', 'component_descriptio...  SINGLE PROTEIN   10116  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#searching and selecting human aromatase enzyme as the drug target\n",
    "target_query = new_client.target.search(\"aromatase\")\n",
    "targets = pd.DataFrame(target_query)\n",
    "targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_target = targets.target_chembl_id[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>canonical_smiles</th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>standard_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CC12CCC(O)CC1=CCC1C2CCC2(C)C(CC3CN3)CCC12</td>\n",
       "      <td>CHEMBL341591</td>\n",
       "      <td>7100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C[C@]12CC[C@H]3[C@@H](CC=C4C[C@@H](O)CC[C@@]43...</td>\n",
       "      <td>CHEMBL2111947</td>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CCn1c(C(c2ccc(F)cc2)n2ccnc2)c(C)c2cc(Br)ccc21</td>\n",
       "      <td>CHEMBL431859</td>\n",
       "      <td>238.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CCn1cc(C(c2ccc(F)cc2)n2ccnc2)c2ccccc21</td>\n",
       "      <td>CHEMBL113637</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Clc1ccccc1Cn1cc(Cn2ccnc2)c2ccccc21</td>\n",
       "      <td>CHEMBL112021</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                    canonical_smiles molecule_chembl_id  \\\n",
       "0          CC12CCC(O)CC1=CCC1C2CCC2(C)C(CC3CN3)CCC12       CHEMBL341591   \n",
       "1  C[C@]12CC[C@H]3[C@@H](CC=C4C[C@@H](O)CC[C@@]43...      CHEMBL2111947   \n",
       "2      CCn1c(C(c2ccc(F)cc2)n2ccnc2)c(C)c2cc(Br)ccc21       CHEMBL431859   \n",
       "3             CCn1cc(C(c2ccc(F)cc2)n2ccnc2)c2ccccc21       CHEMBL113637   \n",
       "4                 Clc1ccccc1Cn1cc(Cn2ccnc2)c2ccccc21       CHEMBL112021   \n",
       "\n",
       "   standard_value  \n",
       "0          7100.0  \n",
       "1         50000.0  \n",
       "2           238.0  \n",
       "3            57.0  \n",
       "4            54.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#selecting compounds that can bind to the target protein with standard type as IC50\n",
    "activity = new_client.activity\n",
    "bio_activity = activity.filter(target_chembl_id = selected_target).filter(standard_type = \"IC50\")\n",
    "df = pd.DataFrame(bio_activity)\n",
    "\n",
    "#removing instances with missing standard values(roughly 3% of the entire dataset) and instances where standard unit is not nM (roughly 1% of the entire dataset)\n",
    "df2 = df.copy()\n",
    "df2.standard_value = df2.standard_value.astype(float)\n",
    "df2 = df2[df2.standard_value.notna()]\n",
    "df2 = df2[df2.standard_units=='nM']\n",
    "df2.to_csv(\"compound_data.csv\", index=False)\n",
    "\n",
    "#selecting features that relate to the chemical structure(canonical smiles) and compound biological activity(standard value)\n",
    "df3 = df2.copy()\n",
    "sel = ['canonical_smiles', 'molecule_chembl_id', 'standard_value']\n",
    "df3 = df3[sel]\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3.to_csv(\"preprocessed_compound_data.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to cap the standard values between 1*10^8 nM and 1*10^-12 nM so that log10 function does not encounter zero and ligand efficiency is not 0.\n",
    "def norm_value(input):\n",
    "    norm = []\n",
    "    for i in df3[\"standard_value\"]:\n",
    "        if i >100000000:\n",
    "            i = 100000000\n",
    "        elif i == 0:\n",
    "            i=0.000000000001\n",
    "        norm.append(i)\n",
    "    \n",
    "    df3[\"standard_value_norm\"] = norm\n",
    "    x = df3.drop(\"standard_value\", axis=1)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = norm_value(df3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def LEcalc(smiles,standard_value,verbose=False):\n",
    "    \n",
    "    molecules = []\n",
    "    HAC = []\n",
    "    le = []\n",
    "    \n",
    "    for i in smiles:\n",
    "        mol = Chem.MolFromSmiles(i)\n",
    "        molecules.append(mol)\n",
    "    for j in molecules:\n",
    "        HAC.append(j.GetNumHeavyAtoms())\n",
    "    for k in range(len(HAC)):\n",
    "        IC50 = (float(standard_value[k]))*10**(-9)\n",
    "        le.append((-1.4*np.log10(IC50))/HAC[k])\n",
    "    return le"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <th></th>\n",
       "      <th>canonical_smiles</th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>standard_value_norm</th>\n",
       "      <th>le</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CC12CCC(O)CC1=CCC1C2CCC2(C)C(CC3CN3)CCC12</td>\n",
       "      <td>CHEMBL341591</td>\n",
       "      <td>7100.0</td>\n",
       "      <td>0.300343</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C[C@]12CC[C@H]3[C@@H](CC=C4C[C@@H](O)CC[C@@]43...</td>\n",
       "      <td>CHEMBL2111947</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>0.261802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CCn1c(C(c2ccc(F)cc2)n2ccnc2)c(C)c2cc(Br)ccc21</td>\n",
       "      <td>CHEMBL431859</td>\n",
       "      <td>238.0</td>\n",
       "      <td>0.356646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CCn1cc(C(c2ccc(F)cc2)n2ccnc2)c2ccccc21</td>\n",
       "      <td>CHEMBL113637</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.422574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Clc1ccccc1Cn1cc(Cn2ccnc2)c2ccccc21</td>\n",
       "      <td>CHEMBL112021</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.442376</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "                                    canonical_smiles molecule_chembl_id  \\\n",
       "0          CC12CCC(O)CC1=CCC1C2CCC2(C)C(CC3CN3)CCC12       CHEMBL341591   \n",
       "1  C[C@]12CC[C@H]3[C@@H](CC=C4C[C@@H](O)CC[C@@]43...      CHEMBL2111947   \n",
       "2      CCn1c(C(c2ccc(F)cc2)n2ccnc2)c(C)c2cc(Br)ccc21       CHEMBL431859   \n",
       "3             CCn1cc(C(c2ccc(F)cc2)n2ccnc2)c2ccccc21       CHEMBL113637   \n",
       "4                 Clc1ccccc1Cn1cc(Cn2ccnc2)c2ccccc21       CHEMBL112021   \n",
       "\n",
       "   standard_value_norm        le  \n",
       "0               7100.0  0.300343  \n",
       "1              50000.0  0.261802  \n",
       "2                238.0  0.356646  \n",
       "3                 57.0  0.422574  \n",
       "4                 54.0  0.442376  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#computing ligand efficiency values using LEcalc function     \n",
    "df3['le'] = LEcalc(df3.canonical_smiles,df3.standard_value_norm.tolist())\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ligand efficiency cut off for hit compounds is set to be between 0.2 and 0.35. Hit compounds are compounds that show some activity against the target protein and can be chemically modified to have improved potency and drug-like properties. The binding of hits to the target does not have to be extremely good as this can be optimised further after hit identification. This broad range of ligand efficiencies chosen is due a large range of heavy atom counts (HAC) among all the screened compounds. HAC is a proxy for molecular size. The optimal ligand effiency cut off depends on the molecular size of the screened compounds. The details of calculating target ligand efficiency values can be found in this paper. [⁵](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3772997/#FD2). A larger range of ligand efficiency values also allow for a more diverse set of hits to be investigated. This can potentially lead to drug molecules with novel structures compared to marketed drugs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "bioactivity_class = []\n",
    "\n",
    "for i in df3['le']:\n",
    "    if i>0.2 and i<0.35:\n",
    "        bioactivity_class.append(\"active\")\n",
    "    else:\n",
    "        bioactivity_class.append(\"inactive\")\n",
    "df3['bioactivity_class'] = bioactivity_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 350x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bio_class = []\n",
    "classes = ['active', 'inactive']\n",
    "for i in classes:\n",
    "    frequency = bioactivity_class.count(i)\n",
    "    bio_class.append(frequency)\n",
    "plt.figure(figsize=(3.5,3.5))\n",
    "ax = sns.barplot(x=classes,y=bio_class)\n",
    "ax.set(xlabel='Bioactivity class',\n",
    "       ylabel='Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculating Lipinksi Rule of 5 descriptors\n",
    "def descriptors(smiles,verbose=False):\n",
    "    \n",
    "    molecules = []\n",
    "    for i in smiles:\n",
    "        mol = Chem.MolFromSmiles(i)\n",
    "        molecules.append(mol)\n",
    "    \n",
    "    i=0\n",
    "    for j in molecules:\n",
    "        \n",
    "        desc_MolWt = Descriptors.MolWt(j)\n",
    "        desc_LogP = Descriptors.MolLogP(j)\n",
    "        desc_NumHDonors = Lipinski.NumHDonors(j)\n",
    "        desc_NumHAcceptors = Lipinski.NumHAcceptors(j)\n",
    "        \n",
    "        row = np.array([desc_MolWt, desc_LogP, desc_NumHDonors, desc_NumHAcceptors])   \n",
    "        \n",
    "        if i==0:\n",
    "            baseData = row\n",
    "            \n",
    "        else:\n",
    "            baseData = np.vstack([baseData, row])\n",
    "        i=i+1\n",
    "    \n",
    "    columnNames = [\"MW\", \"LogP\", \"NumHDonors\", \"NumHAcceptors\"]\n",
    "    descriptors = pd.DataFrame(data = baseData, columns = columnNames)\n",
    "   \n",
    "    return descriptors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "index=np.arange(0,2806,1)\n",
    "df_lipinski = descriptors(df3.canonical_smiles)\n",
    "df_lipinski['index']=index\n",
    "df3['index']=index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>canonical_smiles</th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>standard_value_norm</th>\n",
       "      <th>le</th>\n",
       "      <th>bioactivity_class</th>\n",
       "      <th>index</th>\n",
       "      <th>MW</th>\n",
       "      <th>LogP</th>\n",
       "      <th>NumHDonors</th>\n",
       "      <th>NumHAcceptors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CC12CCC(O)CC1=CCC1C2CCC2(C)C(CC3CN3)CCC12</td>\n",
       "      <td>CHEMBL341591</td>\n",
       "      <td>7100.0</td>\n",
       "      <td>0.300343</td>\n",
       "      <td>active</td>\n",
       "      <td>0</td>\n",
       "      <td>329.528</td>\n",
       "      <td>4.28820</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C[C@]12CC[C@H]3[C@@H](CC=C4C[C@@H](O)CC[C@@]43...</td>\n",
       "      <td>CHEMBL2111947</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>0.261802</td>\n",
       "      <td>active</td>\n",
       "      <td>1</td>\n",
       "      <td>315.501</td>\n",
       "      <td>3.89810</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CCn1c(C(c2ccc(F)cc2)n2ccnc2)c(C)c2cc(Br)ccc21</td>\n",
       "      <td>CHEMBL431859</td>\n",
       "      <td>238.0</td>\n",
       "      <td>0.356646</td>\n",
       "      <td>inactive</td>\n",
       "      <td>2</td>\n",
       "      <td>412.306</td>\n",
       "      <td>5.70542</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CCn1cc(C(c2ccc(F)cc2)n2ccnc2)c2ccccc21</td>\n",
       "      <td>CHEMBL113637</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.422574</td>\n",
       "      <td>inactive</td>\n",
       "      <td>3</td>\n",
       "      <td>319.383</td>\n",
       "      <td>4.63450</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Clc1ccccc1Cn1cc(Cn2ccnc2)c2ccccc21</td>\n",
       "      <td>CHEMBL112021</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.442376</td>\n",
       "      <td>inactive</td>\n",
       "      <td>4</td>\n",
       "      <td>321.811</td>\n",
       "      <td>4.58780</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    canonical_smiles molecule_chembl_id  \\\n",
       "0          CC12CCC(O)CC1=CCC1C2CCC2(C)C(CC3CN3)CCC12       CHEMBL341591   \n",
       "1  C[C@]12CC[C@H]3[C@@H](CC=C4C[C@@H](O)CC[C@@]43...      CHEMBL2111947   \n",
       "2      CCn1c(C(c2ccc(F)cc2)n2ccnc2)c(C)c2cc(Br)ccc21       CHEMBL431859   \n",
       "3             CCn1cc(C(c2ccc(F)cc2)n2ccnc2)c2ccccc21       CHEMBL113637   \n",
       "4                 Clc1ccccc1Cn1cc(Cn2ccnc2)c2ccccc21       CHEMBL112021   \n",
       "\n",
       "   standard_value_norm        le bioactivity_class  index       MW     LogP  \\\n",
       "0               7100.0  0.300343            active      0  329.528  4.28820   \n",
       "1              50000.0  0.261802            active      1  315.501  3.89810   \n",
       "2                238.0  0.356646          inactive      2  412.306  5.70542   \n",
       "3                 57.0  0.422574          inactive      3  319.383  4.63450   \n",
       "4                 54.0  0.442376          inactive      4  321.811  4.58780   \n",
       "\n",
       "   NumHDonors  NumHAcceptors  \n",
       "0         2.0            2.0  \n",
       "1         2.0            2.0  \n",
       "2         0.0            3.0  \n",
       "3         0.0            3.0  \n",
       "4         0.0            3.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combined = df3.merge(df_lipinski)\n",
    "df_combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_combined.to_csv('compounds_descriptors.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert IC50 values to pIC50 values to allow easier understanding of standard value distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pIC50(data):\n",
    "    pIC50 = []\n",
    "    for i in data[\"standard_value_norm\"]:\n",
    "        molar = i*(10**-9)\n",
    "        pIC50.append(-np.log10(molar))\n",
    "    data[\"pIC50\"] = pIC50\n",
    "    final = data.drop(\"standard_value_norm\", axis=1) \n",
    "    return final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>canonical_smiles</th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>le</th>\n",
       "      <th>bioactivity_class</th>\n",
       "      <th>index</th>\n",
       "      <th>MW</th>\n",
       "      <th>LogP</th>\n",
       "      <th>NumHDonors</th>\n",
       "      <th>NumHAcceptors</th>\n",
       "      <th>pIC50</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CC12CCC(O)CC1=CCC1C2CCC2(C)C(CC3CN3)CCC12</td>\n",
       "      <td>CHEMBL341591</td>\n",
       "      <td>0.300343</td>\n",
       "      <td>active</td>\n",
       "      <td>0</td>\n",
       "      <td>329.528</td>\n",
       "      <td>4.28820</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.148742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C[C@]12CC[C@H]3[C@@H](CC=C4C[C@@H](O)CC[C@@]43...</td>\n",
       "      <td>CHEMBL2111947</td>\n",
       "      <td>0.261802</td>\n",
       "      <td>active</td>\n",
       "      <td>1</td>\n",
       "      <td>315.501</td>\n",
       "      <td>3.89810</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.301030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CCn1c(C(c2ccc(F)cc2)n2ccnc2)c(C)c2cc(Br)ccc21</td>\n",
       "      <td>CHEMBL431859</td>\n",
       "      <td>0.356646</td>\n",
       "      <td>inactive</td>\n",
       "      <td>2</td>\n",
       "      <td>412.306</td>\n",
       "      <td>5.70542</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.623423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CCn1cc(C(c2ccc(F)cc2)n2ccnc2)c2ccccc21</td>\n",
       "      <td>CHEMBL113637</td>\n",
       "      <td>0.422574</td>\n",
       "      <td>inactive</td>\n",
       "      <td>3</td>\n",
       "      <td>319.383</td>\n",
       "      <td>4.63450</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.244125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Clc1ccccc1Cn1cc(Cn2ccnc2)c2ccccc21</td>\n",
       "      <td>CHEMBL112021</td>\n",
       "      <td>0.442376</td>\n",
       "      <td>inactive</td>\n",
       "      <td>4</td>\n",
       "      <td>321.811</td>\n",
       "      <td>4.58780</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.267606</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    canonical_smiles molecule_chembl_id  \\\n",
       "0          CC12CCC(O)CC1=CCC1C2CCC2(C)C(CC3CN3)CCC12       CHEMBL341591   \n",
       "1  C[C@]12CC[C@H]3[C@@H](CC=C4C[C@@H](O)CC[C@@]43...      CHEMBL2111947   \n",
       "2      CCn1c(C(c2ccc(F)cc2)n2ccnc2)c(C)c2cc(Br)ccc21       CHEMBL431859   \n",
       "3             CCn1cc(C(c2ccc(F)cc2)n2ccnc2)c2ccccc21       CHEMBL113637   \n",
       "4                 Clc1ccccc1Cn1cc(Cn2ccnc2)c2ccccc21       CHEMBL112021   \n",
       "\n",
       "         le bioactivity_class  index       MW     LogP  NumHDonors  \\\n",
       "0  0.300343            active      0  329.528  4.28820         2.0   \n",
       "1  0.261802            active      1  315.501  3.89810         2.0   \n",
       "2  0.356646          inactive      2  412.306  5.70542         0.0   \n",
       "3  0.422574          inactive      3  319.383  4.63450         0.0   \n",
       "4  0.442376          inactive      4  321.811  4.58780         0.0   \n",
       "\n",
       "   NumHAcceptors     pIC50  \n",
       "0            2.0  5.148742  \n",
       "1            2.0  4.301030  \n",
       "2            3.0  6.623423  \n",
       "3            3.0  7.244125  \n",
       "4            3.0  7.267606  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pIC50 = pIC50(df_combined)\n",
    "df_pIC50.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Mann–Whitney U test to determine whether the difference in a feature is significant for active and inactive compounds. \n",
    "def mannwhitney(descriptor, verbose=False):\n",
    "    from scipy.stats import mannwhitneyu\n",
    "    \n",
    "    selection = [descriptor, \"bioactivity_class\"]\n",
    "    df = df_pIC50[selection]\n",
    "    active = df[df.bioactivity_class == \"active\"]\n",
    "    active = active[descriptor]\n",
    "    \n",
    "    inactive = df[df.bioactivity_class == \"inactive\"]\n",
    "    inactive = inactive[descriptor]\n",
    "    \n",
    "    stat, p = mannwhitneyu(active,inactive)\n",
    "    \n",
    "    alpha = 0.05\n",
    "    if p > 0.05:\n",
    "        interpretation = \"The difference between active and inactive compounds is statistically insignificant.\"\n",
    "    if p < 0.05:\n",
    "        interpretation = \"The difference between active and inactive compounds is statistically significant. \"\n",
    "        \n",
    "    return interpretation,\"p value is \"+ str(p)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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x/gA3btwoiNAF4ZPx3o6h+emnnzTKypQp89IlowVBEApLNi8fI5OUAwYGBq9dl1QqpVGTpjwNecigys7oyDXr7l7GljWL5zJv+e//KV5B+BR9MC00giAIRcW3am2uhSVqlAfGpOBa2h+p9M3/lD599ABXC+2JkLmhLqlJCW9cp1BwEhIS2Lp1a/6/e/bsyZgxY4owIuHfvLctNIIgCO+LkRMmM7BHJ4LiomnhZYlMKuFQUBzX0o1ZuXn2f6rTp2x57v11DTtTzaQmMikdawfNTRQ/RAqlinuP4ohPzsTSVB8fdytk0vd/m4eZM2cSFhaWvy/SwoULX2s2m1B0REIjCILwL3R0dFi1eQdnz5xmzfrVKBQK2vT7ihENG/3nPZjadexMlxWLqFFcia78RQtPVo6CyUeC8ShnyZwZU+nRb+AHuzTFuVsR/LbrNnFJmfllVmb6DGzrR/UyjkUY2b/750Bvc3PzoglEeG2iy0kQBOE1SCQSataqzdxlq1mwYi0NGjV+qw0ldXV1+WHOEsYdj+DUwzji0rLYfzucvusu0KmsAwOdk/GJOMnYnq1Y9eviAnySd+PcrQhm/H5ZLZkBiEvKZMbvlzl3K6LQYwgODmbw4MFUqVIFX19fGjVqxO+/vxiXdPbsWbp06ULZsmWpXbs2c+bMQaFQMGbMGHbu3MmlS5fw8vICXnQ5paWl4e/vz8aNG9XutXTpUurWrYtSqUSlUrF8+XIaNGhA2bJladOmDXv27Cn05/3UiRYaQRCEIlKufAU2HT7F9i2b2HntMucDgljVsyr6OnldG6YGukywN2Pe3rXcrFaTsmXLFnHEr0ehVPHbrtuvPGf57jtU8XUotO6njIwM+vbtS9WqVdm4cSNyuZzt27czffp0KleuTHZ2Np9//jm9e/dm2rRpREZG8t133yGVShk/fjyZmZlERUWxcOFCtXqNjIxo0qQJe/fupVu3bvnle/fupU2bNkilUn755Rf27t3LDz/8QIkSJbh8+TKTJk0iJSWF7t27F8rzCqKFRhAEoUjp6enRrVcf6jZtSdvSdvnJzN/1KWfLyoX/baxOUbj3KE6jZeafniVmcO9RXKHFkJGRQa9evZg0aRIlSpTA1dWVIUOGABAYGMjatWspU6YMY8aMoUSJEtSsWZOpU6dia2uLiYkJ+vr66OjoYGNjo1F3+/btuX79OmFhYQDcuXOHhw8f0q5dO9LT01mzZg2jR4+mXr16FCtWjA4dOtCnTx9WrlxZaM8riBYaQRCE90LAnZt4WWnf68ncUJf0xML78C9o8cmvTmbe9Lz/wtLSkm7dunHgwAECAgJ48uQJ9+/fB0CpVBIYGEj16tXVrmnUqNFr1V2pUiWcnZ3Zt28fgwYNYvfu3fj7+1O8eHFu3bpFVlYWo0ePZuzYsfnX5Obmkp2dTWZmJvr62n/OwtsRCY0gCMLf3L59m6DA+xR3K0H58uXfapzMm3At4cnjWwfw1DL+NzUrBx0Dk3cSR0GwNH29D+zXPe+/ePbsGZ06dcLCwoIGDRpQrVo1/Pz8qFOnDgByufw//2wlEglt27Zl7969DBgwgAMHDvDNN98ALwYTz5s3D3d3d41rdXV1/+MTCf9GdDkJgiAAoaGhdGnRgC0TvyBt/wIO/DSUzk3rEhBw/53cv3nLVvwZmkWuQqlxbPPtWHoO+vqdxFEQfNytsDJ7dbJibW6Aj3vh7cO3d+9eEhMT2bx5M4MHD6ZRo0YkJSUBeUlHiRIluH1bfZzPmjVr8lef/7dkp127djx8+JCNGzeSkpJCs2bNAHB3d0culxMREYGrq2v+fydPnmTlypX/ac0i4fWIV1YQhE9eTk4O3/TpzBh/Q/pVcKCmhx3dy9kzsao54wb1JjU1tdBj0NHR4bsf5zD+eBj3I/M+eOPSslh0IRzTcg2oXqNmocdQUGRSCQPb+r3ynAFtfAt1PRp7e3syMjI4ePAgERERnDlzhuHDhwPkDwi+ceMG8+bNIyQkhJMnT7Js2TIaNGgA5O29FRMTQ2hoqNb6nZycqFKlCnPnzqVRo0aYmOS1oJmYmNClSxfmzZvHrl27CA0NZefOncyaNQtra+tCe15BdDkJgiCwa/tWmrnoYGqg3h1goCuni5cxa1f+xuBvhhd6HNVq1GTZ9sOsWrqQXXduYmbhQP+ffqZ06dKFfu+CVr2MI2N7V9JYh8ba3IABbXwLfR2apk2bcvfuXX7++WdSU1NxcnKiY8eOHD16lFu3btG1a1eWLFnCggULWLFiBTY2NvTs2ZNBgwYB0LZtW/78809atmzJn3/+qfUe7du358KFC7Rt21atfOzYsVhaWrJgwQJiYmKwt7dnyJAhDBw4sFCf+VMnUb3ONrEfqeeZ+NGjR4s4EkEQitKwgX343CEBQ13N73gqlYqpN3IoVcafwBtXUEkk1Grcgm69+72Xgzvf5u9aZmYmISEhuLm5FdizfagrBQvvj9d9X4oWGkEQPnlmFpYkpkdrTWhSs3K5c+M6He0z6FjBDJVKxbnLf9Br1zZWbd2DsbFxEUT84ZBJJfh5iK4WofCJMTSCIHzyenz+Jdvua98Mct2lEIbVdqOUgxmQN1i0hpsVn3vKmfHDuHcZpiAIryASGkEQPnklS5bE0q8Wf9yKzp9lpFSq2HEjlNPBUVQvYatxjYetCU/uXX/XoQqC8BKiy0kQBAH4YcYsdu/YxrR1K5HlZnI/+CEV7A1pXMrhpdfIVbnvMEJBEF5FtNAIgiD8X5v2n/H7zoP0Hz2FlmWKMbiuNw9jtU/ZVqlUZMs0Byjm5ORw5coVrl27hkKhKOyQBUH4P9FCIwiC8A9H9uygiZsp+joybIz1uB4aj7+Lpdo5W29GUb1Re7Wy5Yvnc3z3FspYSFGoYFaiipbd+tG9T/93Gb4gfJJEQiMIgvAPenr6ZKXlta4MruvNzMN3OB4YSQNvBzKyFWy6HIKhni7OWTvoffo4M5eu4vC+3cSc3MqPddS7qFbsXsluE1PadOhYFI8iCJ8MkdAIgiD8w2c9+/LbyJMMtjJGRyZlfPMyhCWkcf5RLA9ik6lRwoZOFd0AeJaayVc9OyFV5fJTXXuNuvqVt+f7FUtEQiMIhUyMoREEQfgHT09PlI6lOPEwPr/M2cKIMk4WxKZk0c7fNb/c2lifmjZKVGnap31LpRKMySAzs/B2lhYEQbTQCIIgaDV78XKWzJvN+CP7UGam8iA0EmOZki9reyH/x0q3VV1M2HXj6UvrylHk7e4sCELhES00giAIWkilUr76diQ+5atiYajHpKY+TGpZjsDoZEbtuEpSRnb+ubEp2ah0jcjK1ZzVlJaVi8zCXiQ075iXlxc7dux4Z/eLiIhg//79+f+uX78+CxcufGf3F0QLjSAIwkutX70C4ycXGFXjxUaKPa2MiUhMZ+bhO0xrWx6A7cHJDBo5gS+mjadmMWMa+zhRzNKI+LQsZp2PZuqy9UX1CEVOpVSQGXofRWoCMmML9F1KIZHKCv2+Z86cyd8B+10YPXo0Tk5OtGjRAoBt27ahp6f3zu4viIRGEAThpQ5u3cDUGpr7EDmaG2JnasCtsHj+fJxGqtSYcxsXMrKuO1IJrL0Ywv3YDPyr1WTGygW4u7sXQfRFLy3gAs+OrEKREpdfJjOxwrpxP4y8qxbqvW1sbAq1/n9jaWn57ycJBUp0OQmCIGihUqnQVWQikWjfGbp8MUtWPwTrcnVo4qDk22qOlHY0p5SDOROa+fJ13ZIUcyvxSScz0dtnqSUzAIqUOKK3zyIt4EKh3v/vXU5jxoxh5MiR/Pzzz1SrVo2yZcsyePBgYmNj88+/evUqffv2pUKFCvj6+tKyZUv27dunVue+ffto06YNZcqUoUGDBqxevRqAnj17cunSJXbu3En9+vWBF11OoaGheHt7c/LkSbW6JkyYQLdu3QDIzs5m1qxZ1KpVC39/fzp16sSZM2cK7bX5WImERhAKmFKp5Oix4yxfuZpTp06jUqmKOiThP5BIJORKXt41Epup4ttxEwm6fpEGHlYax6sWM+fW2WMolcrCDPO9pFIqeHZk1SvPefbnKlTKd7eS8sGDB0lMTGT9+vUsWrSIq1evMnfuXACio6Pp168f3t7e7Nixg927d+Pn58fYsWN59uwZAIcOHWLkyJG0aNGCPXv2MGLECObNm8fWrVtZuHAh/v7+NGvWjG3btqnd18XFhUqVKrF37978suzsbA4fPky7du0AGDt2LKdPn2bWrFns3LmTZs2aMWjQIE6cOPFuXpyPhOhyEoQCdPLUacb88DMqUy9kRrYod99CMvFn5v48kSqVKxV1eMIbsvfwITwhDCcLQ7VypVLFxVgV39Ssye+q7JdcDRY6KtLS0t7pWI73QWbofY2WmX9SJMeRGXofA1ffdxKTsbExU6ZMQUdHhxIlStCmTZv8VpPs7GyGDBlC//79kUrzvud/8cUX7Nixg8ePH2Ntbc2aNWto1qwZAwcOBKB48eKkpaVhYGCAubk5Ojo66Ovra+1qat++PVOmTCE9PR1DQ0OOHz9OdnY2zZo148mTJ+zbt49t27bh5+cHQN++fQkICGDlypXUrVv3nbw+HwOR0AhCAQkODmb4hDk4VOj3t0GP3igVNRg8Ygq7Ni7BxcWlSGP8WGVkZHDt2jV0dXUpX748MlnBDDodN/VnPu/YigGlc/CyMwMgKSObxZei+XzERORyOVmql98rKQcMDQ21HlOpVC/tzvrQKVK1r8nzX88rCK6urujo6OT/28TEhJycHCCvFaVDhw6sX7+eBw8e8PjxY+7fv58X4//34woMDKRZs2ZqdXbs+HqLJTZp0oQpU6Zw9OhRWrVqxe7du2nYsCHGxsacPn0agF69eqldk5OTg6mp6X972E+USGgEoYD8+NNcrEq315jBIZXpYO7dmhmz5rNkwewiiu7jpFKp+GXGFG6ePEw5KynZCpiToKJjv6/o0KXbW9dvaWnJ6h0HWPzLz2y+cBEpKgws7Rj2y3R8ffO+TXuWr8rdqFuUtlf/8Al5lop9SV+15CooKJBZE8eRGReBBBUqQ3MGfjuaWnXqvXWs7xOZsUWBnlcQdHV1X3rs4cOHdO3aFR8fH2rUqEGDBg2wsLBQS1jkcvl/TkANDQ1p2rQpe/fupVatWpw6dYply5YB5HdJb9iwASMjI7XrnrcWCa9HJDSCUECeRDzDxMdM6zFDU1sCA/56xxF9/Bb/MhO9wONMqv1i/6SOKhUL1y/A0saGeg0avfU9zMzMGDd5+kuPj508nX6d21ExPoamnlZIJRL+Co7jVLwuq/6Ym39eYGAA3w/qwfiajpj45k0Dz1Eomf/TGNJSxtO0Zeu3jvV9oe9SCpmJ1Su7nWSmVui7lHqHUb3cpk2bsLKyYs2aNfllx44dA14kHCVKlOD27dtq102fPp2wsDCWLFnyr/do3749ffv2ZceOHVhZWVGtWjUASpYsCUBMTIxa99LcuXORSCQMGzbsLZ7s0yLSP0EoIP829leMDS5Yubm5nDu8h+ae6gNyJRIJX1Z2YNXCOe8kDj09Pdbt2EepHmNY8FCfuUE6uLQfxsY9h9W+cc+aOJYJtRwx0X/R7aEjkzKihhOrF876qAaPS6QyrBv3e+U51o36vZP1aF6Hvb09UVFRnDx5kvDwcI4cOcKkSZOAvPE1AAMHDuTAgQOsXbuWp0+fsn//fjZv3kyjRnlJs5GREeHh4URFRWm9R6VKlXBwcGDRokW0adMmv/WlZMmS1KtXj4kTJ3L06FFCQ0NZuXIly5YtE13Ub0gkNIJQQIo725CVlqj1WHpSND6erlqPCf/NkydPcDfR3gWgI5MizUx+Z7FIpVKaNmvB0rWb+XX9H7Ru116tq0mlUpEdH4mxno7GtRKJBE9jFUFBQe8s3nfByLsqdh1GIjNRTzhlplbYdRhZ6OvQvIlevXrRrFkzRo0aRcuWLVm6dCnDhw/HycmJW7duAXnTsKdOncqmTZto3rw5CxYsYNy4cfkzlbp06UJQUBCtW7fOH3fzT+3atSMtLY22bduqlc+dO5cmTZowceJEmjdvzvbt25k6dSodOnQo1Of+2EhUH9PXgjfUoEEDAI4ePVrEkQgfg0ePHtGxz7fYV+iN9G/fPBW5OURfW8OezctwcnIqwgg/LtHR0Uz/vB3fVHXQenzi6WjWHXo/1vJQKpX0b1ad72tr//lvvBlDq/GLKFu27Fvf623+rmVmZhISEoKbmxv6+vpvHQsU3UrBwsfjdd+XYgyNIBQQd3d3FswYzcgJ01EaeyAxsEaVHoNOxmOWz58qkpkCZmdnxzPy9k/Sk6t/QEYkpmPr5l1EkWmSSqVk65qgUKqQSTVble4nKhnt41MEkRU+iVT2zqZmC5820eUkCAWoRo3qnD22lzljOvJVmxLM/747p/7aTfny/kUd2kdp1NSZTDkZzrPUzPyykGcpfLfrJqGPH7H9j83vzdiUvkOGs/hihEY8+wKeUbVxK7UpxYIgvLkProUmJyeHRYsWsXv3bpKSkihVqhTfffcd5cuXL+rQBAHIGxNRo0YNatSoUdShfPTKlvNn2ootLPxpCgF/XkCZmYqXrQlLOlXARF+HPbsWM/78WabPLfpdjxs2aUZqcjLjly+klBnoSeFOgpJKDVvy9YjRRR2eIHzwPriEZunSpWzfvp2ffvoJFxcXli9fzoABAzhw4AB2dnZFHZ4gCO+Yu7s7Q8dO5JchXfm2emm1Y+1K2/LrpSvcvXuX0qVLv6SGd6dtx8607tCRu3fvkp2dzXA/v1eujyIIwuv74Lqcjh49SsuWLalZsyaurq6MGTOG1NRUbty4UdShCf9Bbm4uGzZsYNGiRYSEhBR1OMIHauOqZbTz0r4GUIdSlqz7bfE7jujlpFIpfn5+VKhQQSQzglCAPrgWGnNzc44fP06PHj1wcHBgy5Yt6OrqUqqU9gWano/41yYyMhIHB+0zJITCN3Lol5w7vIf6HtaYG+oyYtUc4jBg519ntO6HIgj/dPv2bb7u243cpFiOyyUkZeTQpowL3aq82OHazECX5KjEogtSEIR34oNLaMaPH8+3335LgwYNkMlkSKVS5s+fT7FixYo6NOENjB8xjIzbJ1jbs0r+cuLtyhUjKDqJxlXLcSngsVj2W3il27dv07d1Q35uWxY3a08A0rJymXrgJlF/3WV4w7wupjMh8dRu2L8oQxUE4R344BKahw8fYmpqyuLFi7Gzs2Pr1q2MHj2a9evX4+2tOU3zVWsxvKr1Rig8SqWS43u3sq5XFY29UTztzGjuaclvvy5l0OCvCuR+KpWK27dvk5ycTPny5V+6WaDwYfm6T1d+blsON2vj/DIjPTnT25Sn5+rTZOUoSM/J5UBoLls6dy3CSAVBeBc+qIQmPDyckSNHsmbNGipWrAiAn58fDx48YOHChSxe/P70kwsvd/ToUSoXM3/pRm8d/IsxdPniAklobt28wdSRX+NrqsJUF357pqBS4zZ8/d2Yt65bKGLpibhZe2kUS6US6nrZ8/WOWziW8GLh2j/ElGhB+AR8UG36t27dIicnBz8/P7XysmXL8vjx46IJSnhjycnJGOm+PJc20JGRm5vz1vdJS0tj8jcDmVLDip7+drQpbcekOo4kXdzLrm1/vHX9QtHSlb38z5eFoS7+TdqzdudBsaChoFVYWBheXl5cvHhRo3zSpEnUr1+fMmXKUL9+fSZPnkx0dLTaeYsWLcLLy0vjv9zc3PxzNmzYQIMGDShTpgydO3fW2NxSKFgfVELzfABvYGCgWnlQUBCurmKfnA9Fo0aNuPgk/qXHTwVH41+tzlvfZ/O6NXxW0khjFdnOZezYsW7lW9cvFK2kHEjPztV67GRwLIMGffmOIxI+dNeuXaNt27ZER0czY8YMDhw4wNSpU7l58yZdu3ZVS2oCAwNp06YNZ86cUftPLs/7srZz505mzZrFsGHD2LFjB66urnz++efEx7/8b5/wdj6ohKZMmTJUrFiR0aNHc+HCBR4/fsy8efM4f/48AwcOLOrwhNdkamqKgY0LFx7FahzLzFGw9FQgj4Pvv/V9gu/fwdPWSKNcJpUgV2S9df1C0er55TCmHbyjsfLuyaAosgwsKVGiRBFFJnyIsrOzGTFiBNWrV2fJkiVUqVIFZ2dnatSowZo1a0hPT2fhwhcLNAYFBeHj44ONjY3af8/9+uuv9OjRg1atWuHh4cH06dMxMDBg27ZtRfF4n4QPKqGRSqUsWbKEqlWrMnbsWNq3b8+FCxdYs2YN5cqVK+rwhDcwdvpsph24xczDdwhLSCMpI5sDt8PotfoMQ+p6o4wPIyoq6q3u4Ve+MrcjUzXKcxRKFLpiYPCHbsiw4RSr1oxeay+w+twDdlx/wtA/rrAhII29J84XdXhCEfPy8mLTpk107dqVMmXK0KpVq1dOEjlx4gQREREMHjxYY3yfqakpy5cvZ/DgwQBkZGTw9OlTPDw8tNYVFxfH48ePqVr1xY7icrmcihUrcvny5QJ4OkGbDyqhATAzM2PixIkcP36ca9eusXnzZipXrlzUYQlv6Nd5s5nWrjzV3K2Zuv8Ww7ZcIiAqibV9alDH0562ZRxZtGD+W93jsy7d2PM4i5RM9fE4K65E0nPQUFJSUpj14yT6tW9G3w4tWLdqBQqF4q3uKbxbP89fxNFbj7BvPoCscm2Yt/0of128gYGBQVGHJrwHZs6cScuWLdm1axd16tRhyJAhXLt2Teu5t2/fxtDQEC8vzYHmkDcBxdHREYDg4GCUSiWHDh2icePG1K1bl1GjRhETEwOQ/2Xsn+uc2draEhkZWVCPJ/zDBzXLSfh45GRloC+XUaakPbVK2mscN9CRk5GW9lb30NPTY+7qzUwY+gUWymeY6kp5mAJtegyiUpVq9G7XjL5e+nSoYIZSqeL4qbUMOnqY39b/8dIZWMLbiY+P58ihA0gkUho3bYaFhcVb16mvr89XXw0pgOiEj02HDh3o3r07AN999x2XL19m/fr1Wvf+S0pKwsTE5LV+94ODgwEwMTFhwYIFPHv2jF9++YVevXqxc+dOMjIyADRWgtbT0yMrS3R3FxaR0AhFom23PhxZMQVPO1Otxw/fi2TYorlvfR9XV1fW7T5EdHQ0KSkpuLu7I5VKmTp+NAN9DPLvL5VKaFDShoTbkRz7608aNGr81vcWXlCpVMz4YRwhV05Q2yFvCvXItQspUaUBYyf9WMTRCR+rf7bely1blnPnzmk918LCgqSkJFQq1b8mNR06dKBhw4aYmb3YbqNkyZLUqVOH48eP5y/0mp2drXZdVlaWaD0sRB9cl5PwcejcuTOnnyQTnZyhcexhTDKP0yT5aw0VBDs7Ozw8PPJXH35455rWZKqFlxU7Nv5eYPcV8qxethTjx+cYW9ORGiVsqFHChnE1HdEPPsXalb8VdXjCR+r5jKPnlErlS1cgL1++PJmZmQQEBGg9vmrVKn744Yf8f/89mYG8vzHm5uZERUXld00974J6LiYmBnt7zRZpoWCIhEYoMhv3H+Pr7TdZc+4B0ckZRCSms/hkIOMPBbHj6NlCvrv2b2C5SpVYhK0Q/LlrCy28rDTKW5ey5tC2jUUQkfAp+Oe6Lzdu3HjpruvVqlXD2dmZpUuXahyLj49n1apV+S0uc+bMoXnz5moz7MLCwkhISMDDwwNLS0vc3NzU1rjJzc3lypUrBfpFTVAnEhqhyJQsWZJLQaE4t/yCKeee8dPlRCr1Hc+lwCfY2toW6r3LVKvNtdAEjfJN18N58vgJg3p04sC+vSiVykKN41OgUqnQV2VpbcaXSCToKsWYAqFw/P777+zdu5eQkBB+/vlnAgIC6N27t9ZzdXV1mTZtGqdOnWLw4MFcvnyZ0NBQjh49Su/evdHX12fEiBEANG3alNDQUKZOnUpISAiXL1/m66+/pnz58tSqVQuAfv36sXr1anbu3MmDBw8YN24cmZmZfPbZZ+/s+T81YgyNUKSkUikDvxzMwC8HF+p94uPj2bB6BVHhodSo35hBQ4fTv/MpEjOfUaeEFdm5CjZcfkxgZBIz27uSq8xm4/LJLP5lJht37tNoXhZen0QiIRvZS4/nvOJYQUlJSWHT2jU8CrqHp48fnXv0xshIc40i4ePSuXNnVq9eTXBwMN7e3qxcuVLrnn/PVa1alc2bN/Pbb78xYsQIEhISsLOzo27dugwaNAhra2sASpcuzYoVK5g3bx7t27dHV1eXBg0aMHr06PzEvVOnTqSkpDBv3jwSExPx9fVl9erVWFpavpNn/xRJVP9cleoT8nxzyletTSB8+A7v38uaOVPo7G2Ko5k+F0JTOBuvw5J1f/Dnwf0c3b+Lx48eUddBRpeKrtwIT+KXc8/INi5OrtyYqLAAPFztOH5kj0afvPB6xo8YSs3cALxsTdTK70clc9HAjykzfym0e5849heLp47ls5LGeNgYERSTxrbgVEb8+AvVa9YqtPsWlbf5u5aZmUlISAhubm7o6+sXdGjvlJeXFzNmzKB9+/ZFHYrwll73fSn+OgsftfT0dFbOnsqMBi7535xaltLHLy6NaeNH8cuvK+nSoxc9W9SlayUrgmNSmHI+C+d6PyCV5f16OFeEmJCrVKnZiKsXjhfl43ywJvz4M/06taFGfCyNSlqhUsGfwc84n2LEqi3TCQ0N5cj+Pcjkcpq3bldgXY7JycksmTqWGfVdkErzfv5ViutRsZgFYyYMp9yBk2L3dUH4SIgxNMJHbe/unTQrrq8xfsPcUIdHd2/kbyT3PLNfcD4ah5qD85MZlUpFQmQQuTlZRMSla+wjJrweAwMD1u86SLH2w/glSIe5wTq4dfqONVv3MP7bIcz6sjMWN7ZheGkT3/dpw6QxIzS2NPgvNqxZSScvk/xk5jmZVEL7ksb8sWHdW99DEIT3g2ihET5qSfHx2Bq8eJs/iElhxqkoknXsyJC6UqvRZzSuVwUdcxtSMrOIzTXETi9vbEVy7GMeXN6BlXNpjMztMbf3pnnbbhw/vDN/nYmClJ2dTUJCAhYWFhoLcn0MZDIZrdu1p3W7F10A074fQyVlCJWrvVhRtaIrHAy6wvLFCxg45Ju3uueD+3eoYWus9VhJa0P2BNx9q/qF95f48vHpEQmN8FFr0LQ5iw9voowTRCSmM/JoPI71x2Os86If9vjdm7jomzPz7HVUyrwBqtkZyTy8vJOyjb9GJs+bxm1drCw5mY3p1HMQp/7cVWBJR0ZGBj+M/Ibo4NvY6EuJyVDh6F2WyTPnfvDjGF4lJyeH+xdP0bWOg8axpiWtGL93278mNJmZmTx58gRzc3Ps7Ow0jpfwLk3w/X34u2iuSBz8LB2Pytqn8AqC8OERCY3wUStRogS5tiU5ci+Y44/Tsa3xDXId9STBzLksD28GMH38T/TsPQDLjGRC7x6jRKV2+cnMczr6xshsK7H5j2306tHtreNTqVQM6NaBnq4KStZ2zC8PjH7IFz06sWbr7vdyG4asrCy2bd7IlXOncHErQa/PX8wAeV2RkZE4G2t/NolEgok0l+zsbEJDQ0lISMDb2xtj47zWFoVCwdTxo3h0/TzuplLiMpQkys34fuZ8PD098+vp0fdz+rfZRFknc7VuJ4VSxc4Haayd3/M/PL0gCO8jMYZG+OjZOjpzLzKBS1EK9I21T5mUWfvx8HEoa39fwe0/F5KWEIGpTXGt55o7+XHoSMEMDj598iS+usmUtFHvFvGyM8FDEs/FC+/frtFRUVF0bV6PnJO/088uHq+wEwzp2JTjf/35RvWYm5sTn/HydX4S07Lo2aYxv43oybHZ3zLks8b8MPJbFAoFo78eRKmEG0yq7UCvcnZ8W82B0WV1GTewB9HR0fl1mJqaMnj8dMYdC+VKaAJJGdlcfpLA2KOhDJs8UwwIFoSPiEhohI9e0LXzDGvgg73pq/ZQkaBSqmjQoAELfxpLyrMQlIocrWfmZKVhamqi9dib2r99M408zLUea1TClP3btxTIfQrShGGDGVfFktolrDDSk1PKwYwf6xdj8bQJ5ORof820MTU1RWFqR2J6tsax8IR0nkTGMKmKOV9UcuAzP1t+qOWAT/ItRgz+nIwnd6hUzFztGkNdOYPLW7B4zs9q5fUaNmLlnmPEl27N5iQnksu15/f9J6hZu+5/eXxBEN5TIqER3ntKpZLExMQ3+rD8O5kkb7aMs0E2mWmaqwMD5MbdonWr5gB06NCOlUtmExdyQeu5SSGn+XKA9tVG35RcR4cchfZWiuxcJfL3bBuGzMxMlAkRWBrpqZVLpRLqOOpw/PixN6pvyi+LmXoulruRSUBeF9yV0ARGHwxkQiNP9HTUF92rXMycqHtXKG+lvauquJUxYUF3NMpNTEwY9PUwfl74KwMGDxGL6gnCR0iMoRHeG4GBgUyZ8Qvh0YmgUlKutAc6chlnLt9GJTNBlZuOu4sV82dPe6N1Sgws7UnKSGVodQeGHF6MU72RyHRefCAnRdzB38tebdO4Nq1bsmb9FhIi7mHm6APkfdgmPrmMX3FDypYtWyDP/FnPfmyb8hX9K2q2Hu1/kEzv6f0K5D4FJTs7Gz2Z9mTCWA6pKSlvVJ+TkxOrdx5ixeL57Lx6ARUSKtVpiUfkIXydtLeC1XAxJjg2hXpemoOJAZQv2adLEISPm0hohPfCyVOnGT5hDta+HTDxydsF+2ZCBPdPrsav4Zf5Y1+epSXQ+rPeHNqzCXNzc416IiMj+W3lWiKjYqhetQJdOn3GN+MmMeXL7nxfx4Wf6lnw06kfSdN3IlNmglyZRPOGNflx8my1eqRSKds3/87Ps+Zx5Pjv5Chl6MqUdGvXjC+/6F9gz+3v788KczfOhERQ0+3F+J6Tj+LJtPHEx8enwO5VEExNTUlU6aNUqjTWdjkXo+CnuvXeuE5zc3O+Gz9RrWz/1o0olMbIpJrJSWKWirvaG9q4E5GEX+XabxyDIAgfPtHlJLwRhULBtWvXuHz58n/uAvonlUrFuEkzsa/QC10D0/xyYwtHyjb5muCL2/LL9I0s0Hdryuy5izTq+WnWXFp3/4ZjwQaEKP1ZtjuQmg3akJ2Ty4iZS5l2NY3dD1Io7WREMeMUxnzRgksn9zJ96vdIpZq/CnK5nPFjv+PkkR2c+2srJw5vZ/Cgzwt81tHCletI8GrMhNOxTDkdyYQzz0j3bcHcZasK9D4FZeCIcfx0OpS0rLxFCRVKFdvvRONWqR5WVpo7ar8ppVJJamoqfwVEaDmm4vDdcKo3bcuOu7Fqi+9FJKazJiCDAUOGvXUMwsclJCQEf39/duzYUdShCIVItNAIr23dhi0sXbkRlZErKokUSeo0un/WgtysFAJuXaeEd2mGDR+BzhuO+7h27RoKw+JIpZqbFOoamIJKiVKRg1SWV6+JdXEuXN2kdt7RY8fYcfQe9v7d88ssi5VH4ejLZ117Y6CnR3ZOLl06tOTrr4dgYaG5Lgnk7X8zasKPJCWn4F/Gh8XzZxf6zt9SqZRvRo6FkWML9T4FpV7DRphZWDB/1jSU6YnkSuS06vIlHbt2//eLX8ODBw+oVcKKE4ER6Mpk1PW0QyKREJ+WxYJj9zE1MWHMxCmsX72C77duQE+ZSbZKgq2bN8v/WI+pqem/30T4ZOTk5PDdd9+Rnp5e1KEIhUwkNMJr+fOvYyxYcwC7cn3VWigWr99IOeVN2pWx59GFe9TxWUDrXgMZ8/2k16772bNnqHRe/iGko29Ebk4WurIXidI/V8Wft2gl1l5t1coUCgVnt4zD3NoFW6/myOR6bDhwirmLK3D66B58fX3Vzq9WuxEx6Ya4le+OvZElYeH3KVOlMT9O+IbP+/d97ef5FJSvUJEVm3cWSt0SiQSVSsLU1uXYfTOUcbuuoyOToCeX0bNqCdbdSwagZ78B9Ow3AIVCgVQqRaVScevWLUJCQihTpsxHudryh+jOnTvs3LmT8PBwnJycaNeuncbvXmFauHChGAT+iRAJjfBaZs9fhm3pThrdLSWqduHJ0QAqulpR0dWKz8q7Mmb376xzLkbPvq83oLV06dJI0tYDlbUez0yNR0fvxXohGSnP8CiuPiA0NUOBsVz9A+zC1vF4Vu6Afclq+WUm1j1w9mtMrQatSYh+lF/+zbejSNP1wKdS0/wyaxdfLB29mTB1Cp91aKd1zM7LKBQKwsPD0dfXL/QWno+Nh4cHD9MkyKQSOpR3pUN51/xjGdm56Jgbqr0PZTIZu7b9webf5uNrJkFHBvPiVdRq9RlffP1tUTyC8H+rV69m8eLFyGQyFAoF169fZ/fu3Xz11Vf07Vv4XxIuX77Mli1b2LVrF3Xr1i30+wlFS4yhEV5LaqYyf8PGv5NIpGTKTPPHMkilEia3KMPinye/tC6VSsWzZ89ITU0FwNnZGSdLGelJ0Rrnxj6+jqlNcSSSvLeqUpFDwv1djB+t/kElRaE2niI7IxWZTEctmXnO0NQWG9dyLFy4ML9s+94jFPNrpHGuVCbHtUJbvhk++qXP889nmz13IbUatafzF1No3WME9Zt14PSZs691vZDXQtPl8yEsvhiBUvniZ5qVq2D66XCGTZhMWloaO3fu5M8//+Tk8WMcXTWHabXt6FbOjo5+dkypY0/82R2sW7X8je4dHh7O7GmTGT/8a/bt2Y1CoSjox/tk3Llzh8WLFwPkv47P/3fx4sXcuaM5vb4gJScnM2rUKCZMmICDg/YZccLHRbTQCK9FystXdJUqstW+MRvoyjHXUaJQKJDJ1MfFrFqznlXrtpIrN0OVk4mNuZx5M6ewatl8OnX/nGdR9hg5lkORm01a2EUSI+5i4eBJ7KPzkJWIbk4kS3+ZqLE5ZMumddl+7g4Wzn4ARAafw8r55TOE7L1q8duqDaQnxBJ44zIqlRSJljE8ABYOXlw999e/vkYAE6f+xNEbSVj5v2idUioVDBv/C0tm6lKlSqXXqudT17ZjZ/QM9Plh6QJMVOnkKkFhaMmImUuZMvY74kICqOJqQWpWLucfxVDbw1aj9bCTrw3jNq+lR9/XG8g9e9okHpw5RNuSJlga6XJp6xw6LpjFgjWbcXZ2LqxH/Wjt3Lkzv2Xmn2QyGbt27SrUrqdJkyZRrlw5WrVqVWj3EN4vIqERXkvFct5ci36ssR1AelI07kZZXHsaxx83Y3mQqgu6xsQmG/PTrLmMGz0i/8Pk1+Wr+X33FazKvhiHk5udQZc+Q9m3bRWH9v7BufMX2LFrPwYG+vQZNwEPDw8iIyMJCAjA1taW0qW1byY4ZPBA/jzWg/hQJRbOZdDRNyYtPvSlz5ObnUFc5FNKx5yjYwUz7j96+QdeZkoctjbat0z4u5SUFA6fuIqtv/r+QFKpDLtyXZk84xcO7Nr0kquFf2rWsg3NWrYhLS0NmUyGvr4+revXpLFdDs27v+ieHFbfm0n7brLqbBD9arzYx0kikWCjqyA5ORkzM7NX3uvQgX2kXf+TUTWd8suae+tTwzWbkYP6sGnf6yW0wgvh4eEvbeF63iVbWHbt2sWVK1fYu3dvod1DeP+IhEb4VyqViqdPQwm8fBjXci2wKV4OkBAXepuoS79jYJrFL6fTUHl+hlOJqgA4AYeuXSP8m5EsWTAbhULBus27sS7Xl/TkZ9w9sRyVSoVPnf6YerVh2s9zWTj3Z2pUr0aN6urdRA4ODvlNxvfu3SMiIgIfHx8cHV9s5qijo8Pubev59rvR7N4zCZVUj7TkOFQqldZv509vHmRK4+L4OOR90FWyk3AjKggze0+Nc0Ou7Wb3utka5f907NhxMPPSekwm1yE6KVtrq5Xwas8HdIaGhqKMC6V5g4pqxyUSCT+0KEv3lafUEhqAtBzVa+1YvnnlUsaU0RzrZGagi6dePNeuXaN8+fJv8RSfHicnJ65fv/7SFhonJyctVxWM7du3ExcXpzFuZuLEiaxcuZL9+/cX2r2FoiMSGuFf/fnXUSIyrCjfagyRQWe5fXQZAGZ2JVAhZXQTH4adkeH4/2TmOXOX8ly9tYWIiAhSUlJQ6Ntzdss4ZDJdnErVBiTcPbaMnKw07sjlyJUZzJk7T+uaMA8ePKB7ny8Ji04CmT4osjA1kLBv5wZKlCgBwI8zZnM5KA2/5uOQ6egR8/g6d4/9Sun6X+SPwQEIv3+CrIQn1Pd60f0zqJozA7avIVHZE3PHUgAocrN5dGUXvu6WlCxZkrbtO3Ht5l0sTE1YvmwBlSurD2JWKhUgkZKdkUz83f3IUh6jUEmQOVbGqmTe8z4f55OamsqW9b/z9NEDSvtXpG2Hju90Vk50dDQzJ43j2dMHqFTg4OHDmMnTXzqd/X3w26+/0srXUesxmVRCMStjYpIzsP3/nl2J6dnILR3R09PTes3fSbPT0ZVrT3xKW8q5d+umSGjeULt27di9e7fWYwqFgrZt2xbavWfPnk1mZqZaWePGjRk6dCjNmzcvtPsKRUskNMK/WvX7ZiyKN0Ym18HZpy7OPnXzj0Vnx7P91n0M3LppvVZu5cfBQ0doUL8uwdePYensR6lavfKPu5SuR+D5TYQHnuXkrWQqVqjMlauX1JKazMxMWnfsRWquMaUbDkHPMK9VJT05hrpNO3P84GaUSiUHT9/Drsxn+dfZFvdHKpVyeec0jMxskOsakBAZhK2lEU0qlVSLU19HxvIOJfn13Db+OJOGrpEFEmUGXw3ogWsxZ1xKlqdEpfaUatqGzJRntO89EjdbXU6ffLHDdJ06dZg86zdSgnYwvKo15gbG2Jnocyn0Gkv/OoO1iytyuZwjB/azctYk2pc0ppmlAXeOXKfzbwv4ceEKSvv6vdXP6nUkJCTwZde2jKhojkN1awCexofQv2Mr1u0+/N5OcVWqlLxqVwMJoPx/whgck8yyW8nMX7uNBw8e8M3QoShyc5k1Zw5+fpqvcQ6yl7bmhaYq8HdxKajH+GT4+vry1Vdfqc1yev6/X331VaGOn7Gzs9NabmVlVagtQ0LREgmN8K+yc3K0znACQK5Pdq6C3JxMrYdzs9IJCw2lePHi5CoUeFbrqnFOycodiXl4hfScXFQSa36eMZ2x4yfkH1+zdgMJqUrKtxyktvieoaktvk2/44uvR1GyRHHMSjTQqNu6WFmsi5Xl6el5jBvRh9atW2NgYMDcn6dxNeQoFVxetEjo68gwN9Zh1x8rqVqtOpC3aq21kxdVOv6IXCfvG7yuvjH+zYdz78Qq5s2bx7BhwwCwtLRENzGYYkYKjgZEUszSkJBneTO5Rldx5ESuIbGxsayeNZHpDYrlf3jWNzGghquC8UMHsvXPM4XeJbV03hwGlDbGwezFVPhilkZ0KZHN2pW/8eXQ93Oq88AvBvF12000KqXZSqNQqngUn85vQSokklRK+lVi+bYRNKxSDhNJNk1LOyLTlzL0s8bEZqm4+TBc7XWu17I9xy9sob6HtVq9uQol52IUfFuvfqE/38eob9++VKpUiV27duWvQ9O2bdt3ug6N8OkQCY3wr9q0bMxve25gWayCxjFl7B2siukSfv8ENq6aGzaGB57h8DMZEyd+j56hGTK55irCUpkcPSMLnLxrE37/FHMXr2LUmLH5Hzh79h3AyqWMWjKjUil5eusIiVHBKHLSuXs/GEcfcPSurfVbtpGpNZ07d87/95ffjKBvx1MkZDyjnocVWbkKdtyNJdvFPz+ZARg7bgJOPg3yk5m/K1m1Ez//MjU/oXn06BHS7FS+aVUeO9MXm00+iUtl4fEADOxg1dKF9PKz0IhRT0dGfScdDu7fR8vWbTTuVZAe3r1OF3/NhQwruJgz48wJeE8TGldXVxSm9vx5P0ItqVGpVMw4dIeOfb9kzA8vlguo6udJI3dTBtXxzi/rWKE4Gy4+oqJ3ca4Hvxg03vvzLxh85iTJd8Np7mWDrlxKyLMUVtxMYNSP87V2gwqvx9fX971IYAIDA4s6BKGQid9S4V9169IJncQbpCdF5pepVCoeXtxMGw8dpFIZ+iY2BF3YQm52BpA3iyjo/GYMTG1Jz8wGICcrTW2tmL/XlZ2ZgqGZHSWrdsS9Wi8m//gz6enpXLx4kdzsTPSM1WcZ3Tu5BgNTG8o0HkL5lqOp2H4qKpWSBxe3aq1fV/ZiYGJycjKLfpmJngz2PEpl0L5HLHioR52vpjFj3mK1a89duIS5fcl/VgmAjr4xSsmL7wQ7tv1Bj0quaskMgKuVMRVcrUhMTORh4H08bbXvIu1lpU/wvdtajxUkiUyH7FzNafjp2Qp0DQy1XPH+2HPsLIdi9Bi48RJrLzxkyclAeq69iHONVmrJTG5uLtlJcXxRW3OQdvcq7ujkZJCcnJxfJpPJWPr7Jry6jmLOfZh+I4szemX4ZcMeqlSvrlGHIAjvH9FCI/wrHR0d9mxfz7cjJ3D32iGUEjkGcgW2kmeYynOpVMKa3x/F4ezbmPun1wFKJBIpTqXqEhFwGq9SeYN2bS0MiQg8jZO3+m7IUQ8uoFK+SDgsnXxYs34GR05eRWLiikJSjNBbR1ApcnDyrk1y7GP0jMyxdXvRYiSRSHD2qcfto8uIfnQZU5viGJjYAJAYfpMOLfK6o+Li4hjQqTV9vA1oX8kMlcqI62GJbH4UjX+FChotJ36+pTj34Cnm9h4ar0tudgYoX2zQGXjjKt9p6Q4BaOBtz4EHQVR39yDk2VXcbTSTmocJmbjVLvWqH0WBaNOtD7s3zqJjGXu18j9ux9Bt+M+Ffv+3IZPJ2HnkBElJSezbtw9jY2Nmtmql0YJy5swZ3KyMX7r+jK+jOWvXrmXIkCH5ZVKplBatWtOiVetCfQZBEAqHSGgEcnNzmTNvEYf+OoMCKSb6ckZ8M5D69ermn2NqasrKZQuAvHElUqkUpVJJy3rVuRASiFFa3po0vvUH/H8vHhXhAafJSY1k0oSfSUhIwMmlOJcvbCUpJoRifo2QSCSE3vmLqAeXqNljFtGPLhMZdA6VSolE15LIZym4OjpjbVcCO5+mPLyyi4jAM6QmhONSWn1MQ05mKgFnNyKRyshMjScxMpiMlGfYOLrjYavgmyHfAzBt/EiGlzfF0TyvJUIikVDexQIz/RRmTZ3I1Flz1eqdO2c2ju5lcfKulb855nOPru5hYN8XY4KMTUzIyI5Fx0BztlJ6tgIv37L0/+obRnZvxaR66h+2OQolR55m8UchdzcBtGzdhnPH/+TXS9do4WGGQqViT1ASNv51qVmr9r9e/z4wMzOje/eXb4Zpb29PanbuS48nZeZoLM4oCMKHTSQ0H7G4uDhiYmJwcXHB2NhY6zkqlYouPfoTTUnMfHogkUhQ5OYwevoahoRF0Lun5uwlqVTK48ePGffDdJJ1nYlWWpEle0zM1S2E3TuKnpEVuTnpGOoomTFpJKVKlaJV++5Ii7Wgbuk+BF3YytW9M1EpFdiXrEbdPgt5evtPcrMzKNvk6/wp1orcbO6dXIOTIgdLR29KVGzLxR1TUKlUpCfFYGzhhItvfXQNTLlzbDneNXtgYGqTH2dOZiqh55exfNve/G/wsSFBONbWnAHhbm3M/EOnOHLkCJ6enhQvXhwAPT09xo/4ghnzpuBVswfm9p5kpyfx8PIO9HMjmTplY34dXfoOYNes7+hV3l6j/s03Ihm/fCf29vZ0GPQdPyydTZdSZrhaGXInKoXtQWmMn7UIufzNfyUDAgL4bd5Mkp9Fo2tkQq9BQ9XGAf2TRCJhxrzF3Lt3jx0bf0cqkzNkXl88PDRboT5U3t7ehMankZSRjdk/EsyM7FwCo5Np3Vq0xAjCx0QkNB+h2NhYBn41gsiEXKT6lijToyntYU/Xjm1QKpVUqVIlf+XUEydOEpZmho3ni6msMrkOdn5t+XXVarp16YiOjnrLxNOnT/msx2CsynTFoowpFoBVeiIJtzezZO4Ubt+5h6mpKS2aN8XAwICgoCBi0/Ww+X8XkGfVjnhW7ciNwwvwqNSO3JxMEqOCKdNosNp9ZHJdfOv15+aRRVg65g3stHD0xsK+JDbF/UmJCyXg9DqMrFywKe6vlsxA3hgXq1ItWb7qd777digAUonm2JHrYYnMPPOMdAs/vl9yAtI3YmWYycqlc3FwcGD48G9p06Y1nbv15tLJaPT1dPhx0hh69eqlVk/lKlVZa+rGiYdh1HG3zG+p2h8Qi3XZOvnr5bTt2Jla9RuybuUyToQ8xKd8E9bN64uh4ZuPX9m2aQN//j6f/v42WLvok5KZzsafhnO2SjNGjJv4ymt9fHzw+fH97mJ6G12/GMqXvy9lTseK+TO6YlMyGbX9CvXadCri6ARBKGgSlbZRmp+IBg3yxlUcPXq0iCMpODk5OdRr0g5Dz47oGZnnlyfFPCL4wmaKla6PJOUBtav48PO0SfTs9yVxJvW0zuKJDT7BL2M6Uv0fgyJ79vuSaL2q6Bmaq5VnpMRRXHqTFb/OVytft34jS/Y8wtpFff2P1IQIgs5vxtzOAyNLR+zc1FeAfS744lYcvWphZG7Pk5uHsHD0wtTGDcib7XRuy3gqt5uAjp7m+ikqlQrJk+3s3b4OgF7tmjGunD668rwWmydxqQw7moJzvRFqU9OzM5JJubeZ44d3vNbCbM8plUp+W7yA04f2oKvKIUeqS9MOXenep/9r7Sf0JlJTUxnYpgFT6zlp1P3L2TCGLdjwUbW6/Bfz583j158nY22siwSITc2m2+Bv+P77SUUdWqF5m79rmZmZhISE4Obm9lorLAvCu/C678sPcpbTrl27aN68OX5+frRo0YKDBw8WdUjvjc1/bENlVV4tmQEws3XHzK4khhbO2JTrwbmgXGbNWYBSoVBbRVedVOuy5U/CYjWSGQADEyuCH0eoleXk5PDTlB/ISY3TON/YwhHfep/z9PZf6Blo1vecroEZudnpACRGBWNs+WKjQJUKDM1sUebmaL9YpUT6t8/6L4aPZd658PxdnBeej8au+hca6+zoGpgita3Mug2b1cqfPHnCjEkTGD/8a/46clhj1pZUKmXQ18P4ffcR5C5lOBkYz7ifl+NeqiK//rrspc/4X+zcuoXmbgZaE6UupS1Z8+tCLVd9Wr4ZNoz7kQmcDo7mVHA09yMTPupkRhA+ZR9cQrN7927GjRtH586d2bdvH82bN2f48OFcv369qEN7L+w78BfmzuW0HrN1q0Bc6B0ALFwrs/vgMbp1akfCk4taz5ekPtJY3v/f/POjtXPb1oyq4UBK4EGiA06SEqe+YaQiNxuJTEp8+L2X1pkc+wgjcweiH13B2MoFqUyH8IDTnNsyjmv7Z5GR8ozHNw5ovTYh9BqdO7TM/3eNWrX5bPhkvj+XwIILkdyOl+YnZ0qlAqXixUBSc+cy7D3wYlPC6RPHM2twZyolXqKN3iNurvmRLi0akpiYqHbP3NxcvP0qczXckLKtfqBsi3F4NxrJnNV/0rFbn5e/eG/oWXQktkaa6/oA2JroEx8bU2D3+tDk5OSwbvUK+rRvTr/2zRg/fChhYWFFHZYgCIXog0poVCoV8+fPp3fv3vTu3RtXV1e++uorqlevzqVLl4o6vPeCsbFR/low/5SblY5M50X3iUJqhIuLEyHX9pMY/SC/XKVS8uD8ZqLunaFWqWJU9nRh9oxp+ce9PZzJSI7VqD8tIZIyPiXy/52VlUV44G12XHtC11L6DLO/gk/4asKP/kRudgapCRHcPjQbdy9/kmNDyM5I1qgzKSYERU42906uJjL4HO4V2hARdI6oBxeo0mESFVuNpnqnaWSlJxLz+IZ6PPGhGKTdpeNn7dXKGzZpxqaDxxm3che2zq4kx4Zw88hi7h5fzt0Tq7h5eCEJEQFq1xzYtxflvRMMr+5EaFIWow+HsTUgl0ehcTRv0oTc3BeJ0NjxEzF2rYWTV8381i+5rgHeNXty4fojgoODtf583lSVWnW5FJmu9djFpwlUrlm3QO7zocnKyqL3Z63IObWOCf76TKhgSGNJECN7tePyxQtFHZ4gCIXkgxpD8/DhQ5o3b87OnTvx8fF56/o+xjE0V65c5avvf8PWR3MDtttHl+Fdozs6+nkznmKvrUFfrkTXowNPbx0mPSkaqVyHtMQoJCoFbqVqgkyX3IhLmOTEYOnswo5Dx4mNjaVlh94YebTGyCJvF+zU+HAyQ/axb/s6bt26RXR0NLa2towf0IVfu1XFRP9FS8LjuFT6b7yKsYEhBibGSGzLYFqiCfdOrsauRGXs3CuiVOQQdu8EEUFnyc5Mxa1cc549vYV/s2Fc2jmVKu0nqiVnKqWCwHObSYgMwMTaleyUaNq1qMvM6ZNeOtj2wYMHtGnfmRSVBb71v0Amz5sNo1Tkcv/07xib2TK0Ry0+79ebPh1a8J2vnMmHHnA52RwH36ZY/X9MUNzTG1hk32Pvzk3IZDKKe5WndLPxWreLSIgMxDr7Oru2bfqPP+EXVCoV3Vo1ZmhpHbXF/FKzcph4OoaNB45jYGDwiho+TrN+nIR7+Gn8nc3VynMUSiacjuWPI6cLfDzT+0SMoXkhJyeHRYsWsXv3bpKSkihVqhTfffed2Gj0A/O678sPapbT48ePAUhPT6d///7cu3cPZ2dnvvzyS+rX177XyvNfbm0iIyNxcHAojFCLTMWKFfBz1ePeo7NYFq+KRCpDkZvNg4vbSE+K5t7J1Th618LI1AZHawMi0s0wMTSjZNW8WR+xj6+TFPMQj8ovNnnEux5xQceJe7SHjRvW0617Dw7sXMfUGbO5de8YABX8vLDxaYBvxXqYOviga2BGzINzfOHnqJbMABS3MqaCmxPBJjUJvnUYD+NY9AzN8G82jJjH1wk6vwWpTIZSkYuhmS1e1buBSkl82F3ObxmLvqmtWjIDIJHK8K7ZnYs7JlOqVm8yAzexaN5Mra+RSqVi3LdDyHp4DYOcdFwbj8lPZiBvKwaf2n24umsiPbvnrRwcGxtL961ZyN2aUczTiaTohzy98xelavbE2tWf2Adp7Nq9lw7t26JC9tK9r/SNLTm79wLR0dEv3UDvdUkkEhb9vpkRX/TBOicSL1MJT9MgKEOXWcvXf5LJDMDNcyfpUMNSo1xHJqWsuYqrV69SsaL2AejCx2Xp0qVs376dn376CRcXF5YvX86AAQM4cODAW//+Ce+fDyqhSU39/0Z/o0czZMgQvvvuOw4fPszgwYNZvXo11apVK+II3w/Ll85n3YZNbNiyhQch4aRnKylerjleNbqhVOTy8PJ2Uh8e5svxw5m14YbateGBpynb6CuNOq086xH++BRLf55Ct+49sLKyYt7sGfnHt/yxjZGTF1Gh7SQk/99zyVBPBz+nO1pjLGmtT6xFSew9qnF19xQSs1fjVbs/du4VsXOvSGTwBbLS4vGp0zf/Gpvi/sQGHiX4xp9a61SpVCiVCpLCrtOzvWYL1XPrV6/AKe42TSs7cCJMppbMPCeRynBy8yEjI4OUlBTuRKvwaz4x/9nMbN1xKFmN20eX4d98OFZuVVm/eScd2rfF1EiPzNR49I01P1SfPb1No+K6NKxWkSv3H7zRDCptrKysWLNtL48ePSIwMJBqLi7vxb45RUkHzYHsz9nrQ0zMpzu2qChkZ2dz/vx54uLisLKyonr16hpLQRSWo0eP0rJlS2rWrAnAmDFj2Lp1Kzdu3KBJkybvJAbh3fmgEprnvwT9+/enXbt2AJQqVYp79+69NKF5VbPrq1pvPmQSiYRePbpRrowvA0cvxMbnxaBYqUxOyaqdib2+Gj8/P0jbC1TKPy6T6+Z/aP+T3MIdZdQFDh8+TN8vhpGj1EGpyMbbw4XQ8BhKNx+ndq2epRsXQy9SoZiVRl13YrIwcLVFR88IMwcfJDr63Dg0H1Ob4khlciICz1K98zSN66xL1uXhzT/JTI0nJysNiUSKsaUTAPHh95Giws3kGQM/n/rS1+fwjs1MrmpNXmfry7seZDI5KpWKBYuWUbxKD43XRUffGJvi/sSF3sa6WBlycvM+SBf+8iM9B0+kbNNv1VcDzkoj4vYB/hjoy87bkXw/fgIzZ8966f3fhLu7O+7u7gVS14cuV8eAXIUSuUxziOCdJBWNSpdWK8vJyeHLfj25f/kshjpSUnOl9PrqW74cMvRdhfzR2rNnD/PnzycpKSm/zMzMjG+++eadLGxobm7O8ePH6dGjBw4ODmzZsgVdXV1KlSr8LUaEd++DSmjs7fNWYPX09FQr9/Dw4MSJE0UQ0ftt3aZtGDlV0n7QxIOoqCicLCQkJUZiaJ7X9fZ8lk9mWgJZaQkYmjuQm5UGKhWKzGSeJaXTY+BoyjT6FkMzO1QqJVEPLpFwdwOSf+ynY2bnwdGbKtonZ2D/tzEeV58m8CDdCPPoBxhbumBiXQyFIhfPal1IjQ9DqcghPvy+1unkEqkMmVTO5T0zMLV2RaVSkRr3FD1ja7JSolm5dA7t2rZ55RgJHVUOEokEiQTMSEKRm6OxC7hKpcRQloGFhQW37wVh6tJea10WDl7EPL6Grr4RDSqUAaBu3br06dSQpau+x82/JYZm9sSH3SUm8DhLWzohl0tp4+dAjy27oYASGuGFzv0GsWndbHr6q6/YHJaQRqqhPa6urvllCoWCWuW8GVDBnrE9KiGRSMjMUbBg6xK+vnGNhSvWvOPoPx579uxhypQpGuVJSUn55YWd1IwfP55vv/2WBg0aIJPJkEqlzJ8/X2x78ZH6oBIaHx8fjIyMuHnzplofeFBQkHiDaiF96foy/z8ulbJ25SL6fD6EsDAlCj0HUmNDeLztS3xsDbA31eP25USepkrBuQapUaGkKUyo1WZc/hgWiUSKQ8mqKHIyuXloEeVbDFe7h22tYQzaOxsvwyRKO1lz/EE8aZlZtCnliKP8IOfuZhLy5Bk55qV4kPAEa886WDh4IZXr5i2K94/EJPTecUwdfKhYvUt+wqNSKrh36ndUmc/YuvMAHiXcKVOmzEufO0eii1KpQiqVMKSqDVNOL8K59tf5415UKiVRN7cydeSXAFhbWfAkPVHr2jsZyTHo6BmR/vgvhi58sQ3C1Enfc2DbRrxjdxL1FNo5G9BrkG/+FgxSCVp3HhfeXss27Qi+f5dpx/fR3M0AM30dzoSl8SDHlMVrV6udO+fnGbTxNKee14vkR19HxqjGpfli41ESEhKwsLB414/wwcvOzmb+/PmvPGf+/Pk0a9asULufHj58iKmpKYsXL8bOzo6tW7cyevRo1q9fj7e3d6HdVygaH9S0bX19fT7//HMWL17Mvn37ePr0KUuXLuXs2bP07dv33yv4xPTu0YmU0JdMU01+QJUqVTA2Nmbb5jVsXTGNng0c8TFOZnOfSsxoW45v6pdiRc9qzGvrhU7kOSp0mI5DyWpEh1zRqM7RqyZpieH5/05NiODB5R08uXmQOKUZx8NUzDsdhr2ZIVv6VmZgNRda+joyvZk7a7r44pJxg7G+Sbg9+o2HO4Zh5eRD6B317kKlIofoh5fwqtFNrfVGIpXhU6cvuegSa1CVfsNm8Pu6jbxM847d2RfwDAB/Z3PGV5KSfHwyj4/P58HJX8m4v4EfR/WjWdNGAAwf+gUJwX9p1KNSqXhy8wCWPGXjqvmYm5urHW/bqSvuNiYs+6w0faq6q+0IfTgghkq16r00RuHtfDtmAj9v2Mcz33bctK1Ji9FzWb/7YP6WH8/t2fQ7bcu5aK3js3LOzBUtaP/J+fPn1bqZtElKSuL8+fOFFkN4eDgjR45kxIgRNGzYED8/P6ZMmYKnpycLF4pFJz9GH1QLDcDgwYMxMDBg7ty5REdHU6JECRYuXEiVKlWKOrT3jo6ODuHBl1HoWOHgWSN/48mwGzsY2rsdurovBsO6uLiwa/0K5neujIGu+tvCx8Gcjn5W7A88iVf1rtw4NB+HktXVWk8kUhkqVd5CecEXtiCV6eDkXQepPG8RvGQJWCXeZmpTD41WF0dzQ7pWcicrV8HkVmU58yCGSccOkmngSFpiJA6e1UAFQec341RK+27QEokER88aPLlxEK8a3Vm4fBVtW7fQ+AAD6NqrDz/cucn88xdp5m6MjZEOdUqYEC43Y86vK7G0zBvMm5aWRlZWFt7e3rRpUJbdx3ZgWbIhugampCVGEXt3Dz+O+4r+/XprjWnsuHH4ey6nppsFJWxM8ssjEtP57dxjzt0+9C8/QeFtWFtb88W/jINRKRXoahlrA2Cir0NK8qs/lAXt4uI0VwbX5tmzZ4UWw61bt8jJyckbK/g3ZcuW5dSpU4V2X6HofHAJDUDfvn1Fi8xrGDhkFH7NxxH98CK3jixCKtdBqVCgJ8mkbesWQN5U+DFDvyTsURDy7DQsDD211tWpgivb1++HUvUwtipGemJU/ho0AHFhd8jJTOHC9skUL9cMJ6+a+cc8KrUjITII49shWgdqAjQs5cCPB27RsJQjNT1ssTgWQLJ5BSQSKVHBFzAwtUGpVKBnqJmgPKdrYErY/VO4pSdh4FyTlWvWMfybIRrnSSQSps6ax5MnT9i+cS1ZWVl0nNiRsmXLAnDp8lUmTp1FXKoSqVwPmSKZDq0as3zWt8xbtJz4mGT8XJ0ZuW0Zzs7OGvU/J5fLOXT6Au2bNcaMTDxsjHkcn0ZMloztR45jYmLy0muFd8OrXEUuPY6iipuNxrH9dyMY+euCIojqw2dlpTkRQBtra+tCi+H5khyBgYFqXdBBQUFq46iEj8cHmdAI/+7evXtkyGwx1jPE2aceTqXq8uzOASTRlzCXZ9O9SS2khqY8C3+CzNCKXF17zBUhL61PXy4DZd6A4dzsDJSqF1NjM5JjuXdyNfqG5ugZmODoWUPjehMrF3TlL3+7yaUStTElpsZGGHtWRybXJybkKomRQTh51ST60RWsi5XVWkdiVDA+tXsTcGYddiUq8yjk1d/+XF1dGT72e7WyCxcu8dWoGdj5d8fubwOFd5y5xOOnm1mzYvEr6/wnJycnLt66S1hYGLdv36ZUqVIUL178jeoQCs/M+UtoVsWXX+1MsTB8MYX+csgzInINKFeuXNEF9wGrVq0aZmZmr+x2MjMzK9SlNsqUKUPFihUZPXo0EydOxN7enl27dnH+/Hk2bnx5l7Tw4RIJzUcqOjoale6LwYxRF1bRwzWeVrXd87t8AqOSGPFURrG6E9DRM+TJjq/IUSjR0dKKcupBNFK7MqhUKhLCbhPz+BqGJtYocrPJTk+iTMPBGFs6cW3/HK0zjGQ6+oQkKbQO9AW4EPIMP6e8eJVKFakyK+z/v6O2m0VL0hIjCbm2l/iIANKTYzA0tVW7PjU+HJVSgaVTKSwcvbi692d6NtLeFfQq30+dhX35HhoL41m4Vubira2EhYW9slXmZZydnf/TdULhsra2Zvn2Q3zZtR2uJlKczQ24HZmM1MKBQ2c1x4oJr0dXV5dvvvlG6yyn57755ptCHRAslUpZsmQJ8+bNY+zYsSQlJeHp6cmaNWtEovqREgnNR6p06dJI0pYD1chIjsVLFkprX/V1SrzszRha253fH1/C2qsuOiVbsuDYSUY08uH0o3hWXY0nTWoCimzCI8PwbN6VRxc3YmXrSImaA1Eqc5HrGqqvtZKdpjUeiUTCsxw9lp97zMAabsSkZJCRrcDZwoisXAWbLofwc7vyqFQqZh5/jF7JZmrXG5k7IJXrYmzlzOVd0ynm1xBHr5qoVCoig86RHPOI0vUH/P9eUlx8GmBkpH3Lg5eJjo4mJccAm5es8mvgVJXlq9cx+fuxb1Sv8H4rV64c5++H8OjRI0JCQphQoYLGAG/hzT2fkl2U69CYmZkxceJEJk6cWOj3EoqeSGg+Ura2tni5WvAkNoT08OsML6e9T7uBpw0rd1wg2dwJybM7nIxL4tRvZ8GuIsXqfY3p/xeTM02M5OremZQtXZIH0UncP7MOmUyHzLR4JMpccpNCyVZAZnY20Y+uYOeuvrR8akI4OpburLkRwPo7qVg5lESuZ0ZKVCCZKc9wtjBi7O6bPE7Iwqx8LyyKa+7ybW7rjuGDK+TWG0T0w0tc2vEjtu4VsHWrgJt/C7Vzja1cePw0XKOOV0lJSUGi8/IkSNfAhLi4p29Up/DhEIsTFrzWrVvTrFkzzp8/z7Nnz7C2tqZatWrvbKVg4dMiEpqP2G+Lf2HQ199xIfwqun6OWs+RSiAzOZoSIesY37I4Bjo21PwtkIo1P1dreTEyd8Cnbj+u/rmU2j1/Qa6jT2zQSbJvn6N7ZVdKWLlzOSyVnTcjCDyznuSYEIqVaYRUpkv4/VM8urYHuZ4hMrkRZi7lyAJMi5XHsVJ3nt75i1SlgtDIINKV0bi6V9caa25GHGEKY0o7l8baxZdbfy3Fo/JnaovipcaFc/3QPBS5WdzWlVGjSnmaNm36Wq+Xi4sLqozolx5PjQ6gXg/tsQmCoJ2Ojg61a2ufnSgIBemDWodGeDN6enqs/m0hzVs2Z+edWK3nXAtNICE1nWnNSmCkJ+dCyDPMnP20jnOxcvZFV98YuY4+Kc8eY/V0B1s/r067ss6Ucbakf9VibO1bEQ9zSEkIJ+jcZi7umELG/R10KG2JTJGFjas/zqXq4Fy6HvHh97n152KcvOuQEBmIrr4JKkUuaYlRGvdWKnLQy46kes3a5GanA+BQsjpPbx/OP+fSzqncPr4M7xrdqNZxCt71vqL/sOk4uL7eAlp6enpUq1CKlJhgjWOKnCyUcTdp26bVa9UlCIIgvFsiofkEuJfw4GQ43IlQn3GQmJ7NzDMxWJia5CcwebONlC+p6cUspPir65nWSjPxMdCVM6pecRTxDyhd73NsiGNj74o8ScrGo2ZvPKt2xNDMDgMTG9zLt8K1bDMeXNyKqU1xkmJDKNvkawLPrichMii/zoyUZ9zaNx29rGdYmBpzedePnN/6PQ8v7yAy+AL3z6zj2oG56OgbU7XDZGyK+6NnaI65vQflW4zA0q0G5SpozrzSZub0ydjk3iE24BDZGcl52zA8vU7cjdWs/vUXZDLt+1wJgiAIRUt0OX0C2rZuwZqtx5hyLRmbSw8obaPH06RsHqYZkO1QixqKM6hUKgKjk5EAyeF3UKmUGnspxT6+gY5B3topxrlxmBpo326iYjELLPQkPHt4ngHVi2NtrE9Qsg4V/zGuBvJ2rX5y8yBKpQITKxeMLZ0o0+grQu8eJfTOnyhysslMT8QkM4zANGNywk2o1mk6EomEpJhHBF34g2dPbqLIzaFaxylaW5bcyrfk7KYz//o6qVQqrl69SusWjVHk5nDmwhVS49Lo2LIu3bqOU1uIUBAEQXi/iITmE+Dk5ETF0s7ciTEBO2+uJMeiV9wM08wU7p1YxQPjTL7bdgU/ZwssDHTxMkrl9pEFlK7/Zf6eTUkxj7h/+ncc7F3JSk9C8YpdqhVKFbkKBTmR12jR0hGlUolM/+UL4ukZWRL18CK1us0GQK5rgJv/ix3CL++ejlxpQKn6IzC1fpFEmdm6o2tggneN7tw9sfKli+5JJFKMrZy5cePGS6dr3rp9h8HDxpJrUBypoT3K9EjkGU9YMm8GZfx8Xxq7IAiC8H4QCc0nIvppIDdOnMfQxBpDMzuy0hPJSI7GtmQdDDMvMKtDRaTSvCSlTbli/HHtKXO2fIfUyBZFThYGymQO9ClFWraCH4/+SLqePWEJaThbGGnc6+D9GOIyVUhS4jhyLxVrYz0UWSkvjS05NgRDUztSnj3BzK6E5gkqFYkSS7yt1VuE0pOi0Te0wMTKBf5lo0eVUoGenp7WY/Hx8fQbPArb8n2RyZ+3wviiyMmi/+BRHNmzQWxQKAiC8J77TwnNjRs3OHv2LLGxsVhZWVGvXj18fcW32PeVb5lKpEcFM6xmMbpXdidXqUJXZsmFEFN+PHCIH/rVzE9mnutUvhg3Q+NpV86Iiv9YJnxFp9K0WHSc77brsqJnVYz1XswyehyXxoLjgXiagKNhAkZ6zkQmpaOXFUN8+H0snUqp1ZUSH0ZaQgROPvUIDzipNaFRAUaWmrO04sLuYOtWASAvQUuJw8BEc3q6SqkgNT6cUqVKaRwDmL9oGUZujf+WzOSR6ehhWLwhCxb/xsQJo7Veq01WVhY/TpvGn3v3INeR8+U3w+nWravW7rCioFAoOLBvL5fPnMTWwZGuvfu99lL1giAI76s3SmiUSiVjx45lz549auVLliyhV69ejB0rFhx73xw7doyYyFA+K21H3+olAXj+sV3Tw5aprcvx85E7/Nxec3xLpwrFCIxKpqLri/1WIhLT2XsrFGM9KdXcrBiy+RJWxga4WhpzNzKR0LgUZAol09pVxc3qxV5FrcsWo+3qZSS61sfFtyFSqYzwwDM8vXWY8s2/4+ZfizHXkswkRj3A0NSW5GePNcb1SCRSVP/fgsG5dAPuHFtGhVajkUrVB+4GnttEWZ+8ukNCQpgzfwnhETG4ujgy/JsvuXLjLqZuHTXu/eTWEZ7ePsKto1J+Xb6GGZNH06dPn/zjR/86wsbfFqHKSgcdfTr2HUSp0r60rF+bVqVsmNvYkYwcBWuWTmf+zJ84e+Vqka+/ERYWxtA+XajvIKW5kzFRD24xvMs2Gnb9nJ79BhRpbIIgCG9DolL9S1v936xcuZJZs2Zpr0giYcaMGbRt27agYit0DRo0AODo0aNFHEnh8fKtiOTZAzZ9XgszA12CY5J5GJuCtZEe5YtZIZVKaPfrcVwcXUjJ1UVPkkNXP1PqeNhw5UkcT+JS6VDeFZVKxaLjAWTmKvisvCu2Jgbcj0xkzfmHXI3JRVcmY2AVB2yN5DyOT2dAzZIasahUKposPE6iXl4XkXXxcpSo2BapVM6F7ZOQSOVUbDUKmVwXlUpFXOhtAs5uwMyuBFZOpclMi8e9/Itp05lpCTy4uBXf+gMBuH5oIWkJYbiWaYKZXQkykmN4fOMg5oYqgu5eY8HiZazdfhxzj4YYmNiQnhRN0sO/UGXG4VD1KyR/S4Su7pv9/72lVORkpgIqsjPTqFjOi2NHDjB3xlTiLx+kWxlb9HRkZOcq2Xo7hg1XnjKvtTeedqZqz77xShj39NzZuHlTofycX1e3lg0ZWc4AMwP11qhZZ0IZ+svvL23FEj4cb/N3LTMzk5CQENzc3NDX1y/o0AThP3nd9+UbtdDs3LkTAGNjYwYPHkzJkiW5f/8+S5cuJSMjgz/++OODSmg+BYlJKdjJJWTkKPjxwBU8bE3xdTTncVwqGy+HYGthQbpNFXIr9cRUzwhFbg5L7h7g7JObJCYlMKpx3gfc5iuPcbc2wdfJHH0dGUZ6cioWt6aCqxUj99zD1kiH7pVcab3kKOv71tKIIytHwZWncXjZGhNZrDFO3urnqJQKrJz9uHt8BRKpFKUiF3P7kriXb0VGSiyOXjV4fPMg1w/Nx6FkNWQyXSKDz5EY/ZC4sHtYOfvg3/RrcrPTubp3No+u7UGHHC6eO4GHhwcBAQGs23EKB/+u+fc0NLPDsHx37h2ehd7TG1gVz+u+SkuKJiMlFolEik+dflg45CVnidEPuX7sN9auXcu9k/sZW8spvy5duZQ6xU04dl+mkcwAdC7vROd159/+B/oWbt++jbtuBmYGmoOn+5azYcX82cz5dWURRCYIhW/JkiWcP3+edevW5ZcdO3aMxYsX8+jRIywsLGjSpAnffPONSOY+UG+U0ERFRSGRSPjhhx/y9+GoVasWpqamTJo0icePHxdGjMJ/EB0dzfqVvyFT5ZCerWTKvptMb1sec8MX38wbeDvw2fZofFsOyi+TyXWwL9uGi+cTSY2MQCaVoFSq2HE7BomRHdIoc5Q5GeimBjOihg3+zuaMb+TB/KP3AcjIUSGXSUlIz2LWyTAepuiQnhqPrU4mbco40bacMwfv7+FJ1CVsqg1CR8+I1IQIsjOSKVmlQ34c2RnJIJGiq2/MzSOLUCpyKF62Gc6l6hL79CZJsSEkxYRQqc14Qq7tITzgFI5eNZBIpJhZ2dKwTgVW/jofAwMDAGbPW4pFycZaXyvXKj14cm45+qZ2GFk6c+/EKgAqtRmLrsGL5MTcrgRV2n3PN6O+Z22HvI0zU7NyWHAmnFtxElKyVVS21P6HUCaVYKxTtMs+PQgOpqSp9nE81sb6JD2IeccRCR+7sLAwtm7dyoEDB0hOTsbU1JTmzZvTsWPHd7pZ65o1a1iwYAGVKlXKL7ty5QpDhgxh2LBhNGnShCdPnvDDDz+QmJjIjBkz3llsQsF5o4QmMzMTQGPq6/M3SWpqasFEJfxnSqWSVk0aEHDjCg5mBpQz1+V+pIJWZZzVkhmA/fef4ejfTms9juXacfHRHT7fcocKzqbkutSnWIUXCYdSkcuUE78wp24K7jYmKP7fc5mVo2D7jXD2BGdhXmMokuBj9PN+Srsy9vnXNvB2JDwhna8PzsKq+lfcODSPktW6ABATcpWIwDPoG1uiUqnISktAR9+E1PhwTG2KI9c1wMGj6v/PvULgufXkZKZj6exNZmo8uVlpJMU8YcGcdfnJDEB0bDx6xc21PquRuQPe3p44Gz7g5vW/SEsIw8KxlFoy85yOvjGmdp7EpCRTPMuYAdsfYljxC+z8XbHOzSH8jPbdhRVKFanZL1uw8N1wL1GCHakqNNvPIC4tC1NLm3cek/DxOn/+PCNGjEChUKBQ5I11S0hIYPPmzWzdupU5c+ZQrVq1Qo0hOjqa8ePHc/XqVdzc3NSObd68mapVqzJwYF6XtaurK99++y3jxo1j8uTJYt2pD9AbfWXMzc0F0Jj+amSUN3U3JyengMIS3kRaWhpXrlwhODiYz1o25fHd6yzvWZ21fWuxqGsVanna0aiUlllCGQp0DbVPR9bRN8HQzBbTGsPZFZyllswASGVy7KsPYtGFvG/12bl5H9a9qhbn1/PhmFT+Al19EyzT7qklM885WRjSu7Qu17ePJystCX1Dc6IfXiIx6gFlmwzFu2ZPStXqRdkmX6NU5JAUE6J2/bPQ20hlOugZWuDoWQ1FdhZxT29h71EVE7uSnD17Vu18S3OTvFYfLTJT43FysGfZ4rmcObKVnp1bY2Sufe8rABNLF3bfCmfp+XD0K3yOsXXeLDCZXIdEXRfuRmreZ+3lULzKV31pnf/myZMnjB76Jf07NGdg1/Yc++vPN66jbNmyBKbrkZKp+Xv6+41Y+n094j/HJwh/FxYWxogRI8jJyclPZp5TKBTk5OQwYsQIwsLCCjWOu3fvYmZmxp49eyhbtqzasX79+jFq1CiNa3Jzc8WX8w9UgbSBvy/TUT81CoWCH0Z9y1cdGnFs9resHNmbO1cv8kvHShS3Ms4/T0cqJStXs3WgejFj0kOvaq07LvQ2qQnh3NozFRtHT63n6BqY8jhVRmxKJiHxWahUKvrX8CRHboKJlQtxIZcobaFk+fkwjgXFoFCqjz9v4etISTMlBibWRASdJSLoHCWrdlJ7P0kkUnzrDyD2yfX8svTkGO6eWEWZhl9SqlZPnErVwbNaZ0rV7s29k6swMnNQ+yN648ZNwsPDeXRlp9bnSHz4F98N+xIAuVzOhAkTSIp5pPVcgKSYhzzIMONilAJTG/XdmW0r9+WHM+nMP/WEJ3GpBEQlMXr3HfbeCiM3+gFXL196ab0vc/niBcb2/YxWeo8ZX96AYV5KTi2dyPTv33xW4cxf1zD53DOOBD0jIT2LgKgkpp4MpUKb3mLpBaHAbN26FYVCwcvmnKhUKhQKBdu2bSvUOOrXr8+cOXNwcXHROObj44O394t93rKzs1m9ejWlS5fG0tKyUOMSCsd/WodmxIgRas1x2dnZ+f+/X79+audKJBJWrhQDDQvDuG+HUD4riD61HPLLHoVGUNJWvaukirsNW66E0K+G+syjisUsUJw+S6Z7DfSNX/wC5+ZkEnxxK9U6/kjI9sEYSLJ5mYiEZD5f9whTAx3aLj2OvlyKi7kjaQmRPLlznDRHbzIys1A+TmPuubvMbFqMUvZ58cllUoz15Fikh5OrLIaRhYPW5FgikSLXNeTclnEYmjsCShxLVsPQzE7tPF0DUxw8axB+509q1/4RgD37DjBl9hqsfTuje/cYQRe2ULxcC3T1jcnOSCY++E86NquCp+eLpM3R0RFjeSapCREYW6i31KQnx5CRHItv2/HcOjyXf7bjyOQ6ODcYxfWYh+zdPAtHEx16V3bm5za+ZOcq+X7st/zx55k3+hLwy+SxTKrrjI4s7/uHvo6MXuUdmHvuRP7I/9dVrFgx/jh8it07trH17ElsnZz4+fuB2NravnYdgvBvDhw4oNEy808KhYL9+/czbNiwdxPUK+Tm5jJq1CgePHjAhg0bijoc4T/6TwnN1aua3+qf/4E+f/7FTA6VSiVabwpJTEwMSQ9vUqm6g1q5rlyz0e1BdBLHg6Kp4mZNaccXXUwJ6dlYSZIIOjAFuVNlzO08SI1/SmTwBSzt3Xm4ewzFzAzIzY5EkZutsfBcYvQDyllLWNShLnKZlByFkp3Xn7LiTBARJ1aia2SJRM8c99JVUChyCL9/igE7LvHXAF/0deSEJaRR2tGcgbVsGXvsKRi9fDVefSNzTK1dkUjzns/cXnNaOIC1ix9JD//C2toahULB9NlLsS/fH4lEgnv5ViTFPOLBxT/IyUzHSJbClnW/UaZMGY16Tvy5mwrVG2Ln3QhHr5qAhMigszy+sZ/yLUch1zVApmuCSqlQm+79nExHn4alHBlTv7jaz6aStYSzZ89Ss2bNlz7r34WGhuKkk52fzPxdm5ImbFm7mjETtY/beRm5XE6HTl3o0KnLG10nCK8rOVl79+5/Pa8wpaamMmzYMC5evMiCBQs0uqaED8cbJzRvsGyNUIhOnzpBNRvND7msXCUKpQrZ31b+DU/KoLiVMX/ej2T9xRCczA2JS8tCqVIxqVU50rMV9P/jDIkRF4lPTcdOT0Ffd1Mc/V1ZeSaY0TVtGXN0FvY1vsxvyUmMDCL2wm9s7eGDTCrhWWomenIZnSoWJzAmlcOxurhXaI2pTfH8OLyqdyHCypnvDxxgVhtvVp59kL9ejZkigLCwu1qfVaVSkZ4UTcmqnbl5ZCE2xf3JSkvUem5WeiId2ubtA3Xy5Ekw81JLqs1s3TGzdUelUhF9ZeVLu1kcHR0JfXCLqT9OZ93m6SQmpWLjXonqXWYgleb92tiVqEzo3WMU82ukEW/C9U30a2KnWa+hhIjwMJRKJYcOH+H4qfNYW1nQt1c3ra0kWVlZ6Mm0fynQ15GRnZWh9ZggFCVTU1MSEhJe67yiFBMTw4ABAwgLC2P58uVUrfrfx7kJRe+NEpqAgIDCikN4Q3p6+jzT0qJbz8ue3Tef0t7/xXYFKhXoyqQMa+BDdq6S+PQsTPV1MNTN+/E/fpZCepYCuakthrbOSBQJbLoWSXETKXFpWaw6G0wpPQkRZ2cQnmtAemYmqowE9gysysGAOLYFpKEydkaZk45ZbgyORrrIdPTVkpnnHDxrcOXufqYfvEVUqoKOmx6iZ1EMqUVFsiICeHBpOx6V1QcgP7qyC7sSVZBIJJhYu2Js4UzItT3YFPfXaAF8dGUn6w/9DsCTJ6E8DbjIo1sn0NU3oWTVjhhZOPL4xgGSoh8gk0qpVr8d5X09mDNzKoaGhmp1yeVyJk/6gVEjR1C/VW9s/TurHbf3qELwhT8IOL0Wt/Kt0TMyJyXuCaFX/uBz71xsTQz4pzsJChpaWVOrQWtUZj4Y2niSHZrMjoNDaVqnLJN/UB8X4+7uzoMUldbWzsMPk2n/fVcE4X3TvHlzNm/e/MpuJ5lMRosWLd5hVOqSkpLo3bs3qampbNy4ES8vryKLRSgYYnPKD5RrcTdmXgimkbd6K0ATH0emHbxNyLO7tPcvhkwqxc3KiOuh8aRk5mCir4O96YsP2uxcJV/seoRvm8kYmb+YjRQXeAynrLM8TnjIF7U98fjbuByVSsWgDRc4EBDHzvgS2DV6kYDk5mRycsdY9J20D6qTSCRkSfTZHpiCqYMP5Tt+obZVwa2/lnFxxxRs3cqjUilJjgnB1q0CDiXzvjnlZKTkbarpVpGbB3/Bq1YfDEysyM5IIeT6PnSVqTg5ObFmzVpGT5qNa7lWmDt4kpEcQ8CZ9aQlRmHtWgb3Cm3yE67A+DBad+jO4X3bkMk0u4+MjIwoZm9KUloiekbmasfcK7Un9PQvuKiukBCWTAVXZ4ZOGc7eJdM1kpDo5AwiMOeHaXMxLd0DHb28BMrA1AbsSnDk8jG8Nm+lW5cX2zBIpVK6DPiahWvnMqiSY36X4vnHCcQau+DvX17r6ywIRaljx45s3boVpVKptVVfIpEgk8n47LPPiiC6PDNmzCA0NJQVK1ZgaWlJbGxs/jFLS0utfwuE99tbJTS7du3613N0dXUxMzPDx8dH7FhcgNrUr4FMomLlmWD6VvfI31zyWWoW9yMTScvOJSIxHXszA4Kik3kQk8yXG8/jZm2CTCKhma8TVdxs2Hg1DPMyHdWSGQArr/qc+usqHcoVU0tmIO+PkYWhDtsDMzCvWoeoy+vJTYtBbuKIRammuNUfRuDZdWijUqmQG5iTm5ODX8MvkUgkah/8ZRp+wc0jSzCxKoaekQVu/i3V9m9KirhDK7MgjgZEUsvBjNNnNyDT0UUm18XC3pPatTyIjo5m9KQ5VGo3Eaksb++khIgAZDq6eFbtiL6JNbFPbvDo2h5K1eqFkaUzcYme7Nm7n3ZtW2uNe/G8n2jTsQ/ZxRphYps3s+nJrT+JCT6NQ7GSXL8fRlJkMPrRt7j68CRxadn03nSdPhWdsTfR5UpUJsHZxjTv3IcH267nJzNqr3nJuqxYs04toQFo16kL5lZWTF/0C7KsNHKQUbFuIxaPeP0NM9+lrKwstqxfy6kj+wAJ9Vu05bMu3cS6Hp8QZ2dn5syZo7EODeS1zMhkMubMmfNOF9f7O6VSyYEDB8jJyaF3794ax48ePVpksQn/3Rvt5fRP3t7erz3oVyaT0aVLFyZMmPBfb1fgPtS9nFJTU/F1tae0ozmtyjjz5/1IDHRk5ChUXHkSi4WhHst6VMPCMG+9oGMBkRwNiOTbhj5YG+uTkZ3LxkuPOB8SS1CyHuW6zFNLGp6LfniJ7np/0aqM5i/22gsP2BIkwddKxaAq9hSzNOJhbAqLzkcRZ12TgCsHKNtyFCaW6tdGP7pC9MPLGJrbY2hqQ0zIVWQ6+igVuch1DXCv0IaczBQigs7iXaO72rWPru1D/vQoO/v5k5SRTbdVZ3BuswAdXQMSnlzCXBnCzj/W0b3350QovbEu5odSqeTeqTWocrPxbfCF2vs1Ky2ReydXU67ZMFTKXIxiD/LHhpWEhIQwdPgYnoRG4Ffai/lzfsLa2pqkpCTmLfyVMxeuERkZhcTYFdcK7fPrVORkEX58NvMaWeJqZcTh4DguZ1lTqVoNKlWvScWKlfj2u/HcSfFQm1X2dzHX13HxWMFNZVWpVFy5coXQ0FC8vb3x8fEpsLq1SUpKon+n1rR2llC9uCUq4NSjOA5Hy1j1xx6MjY3/tQ7h7bxPezmFhYWxbds29u/fn79ScIsWLfjss89EwiC8tkLZy0mb182HcnNz2bBhAx4eHnTpImZXvEpWVhZbN63n3ImjWNnY0mfQ15Qo8WIn6lu3bmGoK8NQV06jUo409nFCpVJx8G44cWmZ9KnmkZ/MZOUoOHg3nJntK+R/8Broyulf05PkzFxux6f8v4VEMw6VUsHeW6EUtzLCz0m9dc1MT4cmxbIZWvfFbCNPO1MWtDVl6p8XCNdRcffoMhy8auHoVQNlbg5h94+TmRKHvpktKc+eomdoStkmQ/PjykpP5O7xlbhXbEvs4+soc3OwcvElNzudZ09vkRofTlkrHRRKFWYGunzbwIcxW76jQqUqjB0ykJYtfkIqlXLj1n28m7ZHqczl/B8/INfVp2Kr0RrJt56ROVYuvsSH3cXcwROpVMLXw0ay6/BF3Ct/hnN1ex7HhlCqQkNKe9jjUdITIyMjZk4dw1cjJmHlrz7WR6ajh32tr1lwfg5zWpagSUkrzp8Mp1f/gfmLT1pampEdk/zShEbKq6e6vokrV68xbNQkcg2KodKzRpJ+CGNJPKuXzdW6LkdBmDRqGF/7GuBiaZRfVs/DmmJmKfw4fhQ/zV9SKPcV3k/Ozs4MGzbsvZiaLXz83mphvUOHDlG2bFl0dHTo06cPixYtYtGiRfTo0QO5XI6HhweLFy9mzpw5+Pr6olKp8je4FLQLDQ2lS7O65J5ax1C3DJoQyC9fd2PBrBd7i5QpU4YchQozfR3OP8rr95VIJCw6HoiVkR7V3F8sYX8iKIrmvs5aW9L61fDAz0pK/MNzWmNRhp5maquynHsUy/yj99SOnQ95xpe1Smi97tvaLuiQhZ6xFYqcLB5c3Ma1A3OwLlaW0vU+R5oZj0wmxaV0A7W49AzNKV23Pw8vbQeJBGefuoAK+P9mlXYehKhcaL3+Ab22P+Xk43Qcbe24fOMOrVu1RCqVolQqSYsJ4ekf/Und/xVu8ih0kx+Q+ZJZUTbFyxEfcZ+Exxcp7+fNvhO3Kdd8BKbWrsh09DC2cELX0Iw4ihMqrcTdlJIMGLWQx6GRKJWayYeugSkRmS9W0i5vLePatWsvXvPe3cmIuKg1lszUeLxLFMy31oiICAYN+wEzv17YeDXCtrg/Nj4tkLu1o2OPL8jKyiqQ+/ydUqkk5lGAWjLzXAkbE0Lv3yzwewqCIDz3Vi00O3fu5NatW0yYMIHu3V90DzRs2BAnJydmzpzJw4cPGThwIHZ2dvTo0YOHDx++ddAfs3FfD+CH6taYGeSNN3A0N2REDUMWndrNjUbNKFeuHMbGxkhkcq48eUZ8ehbRyZk8ic/ASE8Hc0M9EjOy81toEtKzNVpXnjMz0MXeREbY44OkmDlgYusBgEqlJObWbho6KbAzM+SLWp5svhzC8cAo6nnljbXJUSi1ro0CYKyng52pIZZNh/6PvfMMjKra2vBzpmYmvfdKEmooofcOggLSBEQE5Cr2iqiADUVQAXtBLDQRkF5Eeu8ktARIIL33Npk+c74fgcA4ieUq9+r95vmXU/bZ50yS887aa72LK4eX4d+kM0a9BhevuqiAvuQa4T0fbfBcpXNd80vv4FZ1S0VWCxKpjODmvSlOTyC263hcvOq6XBfVlFBR8BVqD3fWrVvHmDFjaBXmx9M9wxkdH1E/v3PZZczZOhPfgW/i/ovKK5NBi9moxZMcvv/xDDFdbY0hrx5dSfOeD6Jy9anfpmo9AoVXNOlnNxHd6deTGrVmbHpKBQcH06dTDMcuH8Ezqke9oNPXVlB1eR0r1n37q+PdRKPRsPSb5Rw5fhoXFzWPPzKVLp071e9/b/EnuMXeU59DdBO5kwsy/86s/H4N/3rIPnfgz2AwGHCRN75fIVgc3lQOHDi4Y/ypCM3NaEtDDcZ69eqFKIqsXbsWgICAuhehTufwzWiM7OxsfK3V9WLmdh5o7ctzDz9Yb0R1MCGJ8loDKUVVrDubwTdni9BbpQxqEcj3p271PWod4smpjBK78QAu5JYT6+/Oa/2CsCR8zsVNs8jcs4CKfW/wgF8a07sG1x87ql04O5NyySit4YO9l6nQNe4eDGA010UvmvecTE7SPpRSC2c3vsb5nz/GbNKjULk2eq6bSoFXSAu6jH6DrmPfovOo1ynLSSJuwGP1YgZA5epL23tewmI28PXX33DfqHu5r10I4ztG2YitdmHefD6hMwW73rC7Vs7FnQzsGMbyrz+lojCb8sTVFCauQ19bgba6GKWzp42YuYl3SEtqK+yjNAZtJSGquuiHKIpcqBCJj79ViVRcXExYSBAhzmWUJnxF8bmVlJxbRpDpNFvWfUtgoK1RYkNcSkqm711j2XiyBmPwSIpVPXnmzW+ZPO0xrNa6FhdXUrNw9mh4LI+Q1vy068BvXueP4uTkRJW54coQURTRC0qHmHHgwMEd408JmqqqKgDOnz9vt+/ChbrwcmlpKQCpqamA7bdVB7YUFhYS5NzwR+KhVqDSlTO0QzMWzn8bFxcXPvr2e0Kax9eJC6kCU0AnTmWUIZdJWHI4hRq9iRaBHiRml5NfqbUZT2+ysPRIKvuu5LP06DUeiPejuaqSNSP9WHlfNPe0tDV5U8jqPGm+OpLKmPhw5FIZpRp9g3PNLNNQblFjNZsQJFJ0mjIMej0tBz5J27ueRqFyoyYnscFzRdGKobYCp9tcg/WacpTOng1WBkmkMnzD26JSOZFy5ijjOkQ0OG64twuhHk4Y9bU3riNSnnGC4f3a0q97e54ZfzcL7wrj28FK3oorxv3ih+ScXIlHQHSD4wEoVK4YtbecTs1GHUVHPuHZboHUGsy8fySXyU+/iEQiQRRFnnnhFYbd/wxrjlRSKG0D6kD8PZXs3bqS5d98/rvEjCiKPPrUS/jGT8UjqDmCICBXOuPXfAhp1b589sVSAAQaLpcFsJpNKJW/Ekr5NxEEgV5DR7Irtcxu35Yrpdw15v6//JoOHDhwcJM/teQUExNDcnIyb7/9Nnl5ebRp0wZRFDl//jzLly+vs5uPiqK8vJwnn3wSQRD+UN+Z/2/ExMSwtLLhpNCM0hqaB7rzWK+mPL7yCzZ++ym9o/0IMxooUauRGXQIcme2pNRyd7QT7cN9+Hj/Far1Roqrdby44SydI31pGeTBteJqrhZUUVSjZ8G98TS5UZadW6Elo0xDi0APu+ubLVaq9SYqtUZmb0kk1t+VFzYn89W4tijlt76V1+hNzNmdi17hg0SmoLokExePQOIG3FpiCu76EJmHvsAzqjtyJ9uql+Kkn5BIBAqunagXE3pNOWq3xnsNuXqHkJaWhJuTDKWsce+ISB8Xtu1YiJObD8bqAua89DSd27dm+RtP81bfW3lGsf5y3rvbjfl7UjlZVdToeCqpEUPaRqpFFVaLCVNNIeG+Mr5L0SNxVvHYvM9o36EjAG/PX0hijhy/NrclxPtGUltZwOR/PcmGNcsavc7t7N9/AKtbLFK50m6fR2g8G7au4KknpjOwX3c2n07BI7CZ3XEVWSd47qk7k5j/2DPPM3dWAQuOHaNngLyuyqnATJPO/Zj8r+l35JoOHDhwAH9S0DzyyCM8/fTT6HQ6Pv/ctnrh5lr5ww8/TH5+PlarFUEQGDJkyJ+a8P8ynp6euIY353JhLi0Cbi3JmC1WlhxO5fmBLZBIBOYMjePHhKz6tgEGs4UX159FX3mEbKkL+ZVaTmVcRRBAJpHwwsCWdI7yJae8lhc2nMVTJWdwiyBKNQaa+LlRozex7WIOGaUazueU89G4TnZz23Q+m9FtwxjfKQqNwcSaMxlsSSnlgfVZtPeXEO2l4HKJkUsVUizB/fAyGRAEgZykvTS9UX5dUZBK/tXDWK1mzHJXLmycTVibIbiExmPUVVORvJ3q0hya3fMaBdeOk3zoO2I7j0Xl5kN+ylHyU4+Tef6nukiNIGDSaQhtNQBddTHeMfdQe2whVqtY78nzSwqqdChcwnGyVnD18inUajWP3D+aJzv4N7gU8lL/aLp/dZSwNkNszP8AjLpqYiN82bBmGTqdDplMhlxuG/XIyclh5syZuLq68tP+s/jE2+bnAKg9AsnNhfT0dKKiouz2/5KkKynIXIPttouiFaOuGp2pLirz2CPT2LT1PrTOXjZisKYkAy+hgAH9+/3mtf4dBEHg9fkLKSsrY8+unQiChPfvGuLwoHLgwMEd508JmkGDBvHqq6+ycOFCu9wYT09Pnn/+eYYOHcrevXsRBIHhw4c3aGLk4BbzP/qCFx6bxsrNxxnSMojiGj3HrhfzQJeoeiv9EE9nKm/LYVHKpMwe2prlJ9KY3iyAj/ZfZuVDPe3GDvVyxkkmYd698TzwzRFWTevJ4WtFbLuYw4SOkYxsG8aaM5m8vCmB5we0wM9Vhd5k4YczGRRUaXnlrjigLum3Z0wga67LKDA5c9zanAuWQORBzlRpz6DNPEf83S8gilZqy3NQqFy5dnIdEqmc2G73I1eq0ddWkHZmI5mJm1Ce3wwyJZ5NumPWmkjcsQiZQo2mIo+SjEQEiQSTQYuupoyOI2bVLz2ZTXquHllBRUEqmvIcpHiwP6WAAc1/2QMbqnRG0ks1BERYcPVqwrgHHubpx6dirCzBRelrdzzUufS2cNFyYdcntOg9FaXaHQBNRT6aa1v55oe65Z1fLqPW1tbi5R+GytkTlZsvVosZi9mIKqrQzsAQQObZjP0HD9sJmszMTA4eOoJapeKuuwbh5uZGTJNILHv3g//NBG6RkgubkJeeJ9pThqKqin+NH8lr733Etg0refbFOaScK0CQu4Cphs7xzXh3aV30tKamhtLSUgICAv7ypWBvb2/G3//AXzqmAwcOHPwaf9qHZuLEiQwbNozjx4+Tk5ODXC4nNDSU7t271xvgdOnShePHjzu+pf0OvlyyhI17T6AwamgXoiXSx5VRYzrYRB0MZvtlKX83FTUGE+3DvfFUK0kvqSbK19bh92R6Ce5OCjxVcgQBymsN7L6cb+NRM7V7NBmlGl7emEitwYRZFJnSNZopXZvYRDE+PlFE07teQSpTUJyZSFnuZaRyBSEt+pC8fylJez5FU5yOQi6lNOcSUoWKqPhh9ec7OXvSss80Lv5char8IhqPVqQnH6Fl33/hE9KKUxvfxD+qPZHt7kaudKEoI4GMhK3UlOXgFVTXc0Umd6Jl34c5veltIuNHkHf1CAv3HMTX1Yk2Ibd8XsprDTy2+iSW0D4EdJuCIAhYzEbmLPoRn7IyoGFBA+Dn7kQFUlL3f4CHlx9enu60axnDrPdW4uNjnywM4BMYRVBMF1r0fgiJtO5PzKTXkHRgKbFdx9sl61pNOlxvM5yrqalhysNPkVchIvFoimgxsnjJWvp1a83c11/h9XkfkKXV1vWjqkrnmS4+DOt3K9enWmfk2QfH8tX6HSz/+jNMJhM1NTW4u7sjlUopKSlhznNPYC3LxU8tIa/WildEc95e/IldPysHDhw4+Kfwp5yC/+n8nZyC33nnHVZ8/C4x/u4Euau5XFCJVbTy3YPdkf2iPHrjuSy8nJX0ibX9tv/KpkTmj4xn9+V8FuxKYs6QOPrEBqA3W1iXkMnW8zm0DvEgv0pHpdaIv5uKmYNaNegbojdZmLMlkeIaPSum2kZ7KmsNDPwuBWefSCRSKVaziaBmPfENbwtA7unveTQ8h59TyjlTG4jVbKTtXc82mPdRW1lI8vZ5aC0CMZ3GENy8F8kHv0Hl5mcjgKAulyZh+3t0H7/AZntJ1nkKr5+mtjKfkBb9qEpYhp+zhBAPZ8q1Bgqq9FhCetO011S762dsncGqcc1wa6CyTBRFXj1eyeqf9tvta4yvv/6al974gK73zbNbxjLpNaQcX02rfo/Yzv/cCvZuXV7vojtkxDiMvn1Ru9sKn8qs0/RsrmTn7gMoQ/vg5huJU+IiPhxun7icXlLDcXUbXnvnfZvttbW1TBo+iJc6euDrestxM720lm+ui6ze+rOjEukfzt/JKdiBg7+C/5hTMMBPP/3EypUrSUlJQRAEmjdvzqRJkxg8ePBfMfz/PJs2bWLN5wv5flrPev8YgFMZpTyy6gRLHuiKXCrBahX5OTmfC7kVvHFPGwxmCylF1SilEvQmM1E+LlisIsU1BqyBXfjgsoTFh09j1tcwtGUwSrmE6b2aEuiupkpn5KHlx/B2UVJSo8dTrbARTk7yOidiq1gnbpxuJP7qjGZGrLhM6yEv4uJZt7RjtVpIP7sJo66a4Ga9kAkiTf3d+faintYDn+DU+tcaFDMAzh4BWFU+mMtzkMidEEWRqqI0mvV40O5YJxcvXH3CKctJwju0Vf12F68Q9LU7Ubv7E9K8FyHNe2G1WqnQVlB5aS2hMR4oYhpOglW3Gsc7+zYw/+7mdi/yVReKGTP5id/5KdYx/dHHaNrtgQZFgdzJBYvZVJ9fJooi5dcPMuKu7vVi5tSp01SYvfF1t6948gjvxPfrF9Ck21ScPfwpTj3C/c0bLn+P8nVl1enzdttXfPMV46OVNmIGIMrHmQ5Fxezbs5sBgxx/tw7+OgoLCzl48CA1NTW4urrSp0+fehuP/ySff/45J06cYOXKW33miouLWbBgAYcPH0YqldKjRw9mz56Nl1fDTt4O/t78aUGzYMECli9fDtxqg5CQkEBCQgJTpkzhpZfuTAO9jIwMRo0axauvvsqoUaPuyDX+Uzz/yGQWj2lvI2YAOkf68FNSHnd9coDw0Ah0VjlahR8tpLV8e+w610tqaBvqiUZv5ufLeXg7K8kur+VCXgURfSfifiPP4uyKR9h2MZsJnaJwVtR95PN/uojJIvL+7mS81Aryq7S4OskJ93JBKhHo1sQPo9nKkJZBbEjMYmLnuvyOFWfzCe02rV7MAEgkUqI7jeH8ro8JiO5KWW4ygf1jMck9kMoUqL1CMBt1yBT2eRqainxApGn3ByjPuUj6mY3IFCqksobLil08g6kpz7ERNLUV+Sic3Gz6UUkkEtQu3hhCe1KUeYiw2IbL4X1CW1OjO8fL+/MYEu5ErI+a/Co9P2Xp6DRkDKPGTfgdn+AtrFYL0gbu8yZGXRVX93+Gj18Acms1D90/ysbgbs2PW3AJbt/o+W7BrTHqqnD28AcERH4tmmK/78zhvcxu69Hg0UNivfnshxUOQePgL6G0tJT58+dz+PBhoK6fn8ViYdGiRfTq1YtXXnml0WXbv5ply5bx8ccf07Fjx/ptRqORhx56CJVKxXfffYfFYuGVV17hpZdeYunSpf+ReTn4a/lTgmbv3r0sW7YMAKVSSZMmTRBFkfT0dAwGA8uWLaNDhw71IdC/CpPJxIwZM9Bqtb998D8AN6XMJufjdsZ3CGdnciEZeld81Ap8TdkkFxTRNiCE+SNvGbY91D2aBbuSuLtVMK8MiePJzd+QWdALa/EF2gepuC8+BlGExfsuY7ZYKdLoWfNwL9SKW78CyfmVfHrgCtN7xbI+IZPrJTW8fk8bpq08TpiXMz1j/DmUrcdnYMsG5+oV3IKLe79AMNTy7Yl00nNqyFo7C6OhlvTEbcR2uc/unPSELbQd/HRdb6MWfdBryjm96a1GBVBVcRpR7UfYbEtL2IJJq6FpD3ufE2fvCMqu7sBkqEWutF9aqy5KZeLIkTw0ZRKb16/j0JUkAuLDeO/tsVRWVlJYWPiHvk3ec/fdnL58log29qLAarWg15Sz88cvCQsLw8vLyy6SY0VssFHoLSRw44uDR1hbNp3dQ+8Y+5L268XVRMc1IIxEGl1SEgQQbxjzOXDwZygtLWXy5MmUlpbWf9E1m831+48ePcrkyZNZvnz5HRU1RUVFzJ49m4SEBDvLkO3bt5OXl8eePXvq5zBr1izefPNNNBqNo5HqP5A/Zay3atUqAFq2bMnu3bvZuHEjmzZtYvfu3fW9m24P7/1VfPLJJ/XN/v4XEEWxURM0s1VEihm3mst8PkDJlyOj8VFJGNfB9o9TEASe7dectWczUStkfDisCdLUtTzdBj6+rz09ov3pGePPG/e0pahaz2cTutiIGYCWQR6MbBdOQZWe5we25O0R7Zix4SzLJ3fnp6RcHvj2CHmVukZfiKJoxc0nnGqDlW269nQb/y7dxr1Dt7FvU5F/heSD32DU1RnR6apLuLD7U0RtOYW73yBn8zNc+2kuZpMetUcgqSfW2o1fXZKJtroEd7+6aJHFbCT50HdYjDpcfUO5dnIdhl/0bNJW5XPXgJ6UXd1pN57VYsaQf4wpD05EqVQybuIkXnztLc5dzWL4+EeZ/PyH3PvgTPoPGc3RYw33u/olmzZtQluRT8G1k794NiJXj6xEr6mkXbt2eHt7N/gc7xs1DE1eQqPjF2ecxe3G/SucXCh0imHjRVuvnPJaA19equHx52fand+mS08u5lU2OPbea+UMHe1oHOvgzzN//nxKS0uxWBr21bJYLPURnDtJcnIy7u7ubN26lTZt2tjsO3LkCF26dLERVD179mTv3r0OMfMP5U9FaJKTkxEEgZkzZ+Lv71+/3d/fnxdffJHJkyeTlJT0pyd5O2fOnGHt2rVs3ryZPn36/KVj/ycQRZEnHnuUA1t/RC4BMxLKagycSC+hWxP7b9orT6YT5KEm3NuZlzYmYrJYiQ9rOJqjUsiQSOpyM55ecwpnpZyBvyhhrtGbCPd2sRMzN+nfLJDXtp5jSKtgmgW44+vixPivDxPgrkIigFVfSWXhNTwCYuzOrSy4hiiKhLQaTFT7Wwm9CpUrnUe9zukt73Bm6wIEQcCorSLISc/coS3pEF5XZZVdXsvcnxfgG9iLjOTDaKuKCG89GIXKlcLrJ6nKOkMrHycK9r8LQHZ+LnKPCDreOweZQkVZziXO/fwhrQc8htrdH1EU0WYd5s0vVvP9mvV8s3o56tDuqFz90JSmY8w/yWcfvF1fsiyKImPGT6HapRO+7brXz99qtfDMrEV8Ml+gWzf7Nh8AB/bt4ZuP3kemr6Z3tBuHjq0kN3k//tGdsJj0FF47SW1NCcX5v97LrFvXLriKi9BVl6Bys62+Kko/g4BAbUUebjd6Uvm1n8j65O2sX32cJh4gd3FH6RvKp6s2NpgHMHX640y5dwuveqltWmzkVWg5UibjiXuG2Z3jwMEfobCwkMOHDzf6Je0mFouFw4cP/+Eo6B+hX79+9OvXsOdSZmYmHTp04LPPPmPz5s2YzWZ69OjBiy++iJubW4PnOPh786cEjclkAmjwH+fNEu3GFPq/Q3V1NTNnzmTOnDm/yyYe+NXlroKCgt89zl+BKIrEhQfQPcKdlVO64eWsJK2khhfXn2Xp0VTCvVwI9rxVNrvvagH5lbW8dncbfkrOw0Upo0JrJDm/stEmf2aLyJRlR2kX5o1TA665NXoTXmr7ip6bSCUC0ttKxIe3CaV1iCfDWofy+rZzBLnDxVPfoBrwMkpnj/r7yr60BxevYPKuHCSu/yMNjh3X92HO//wxXca8Qcqa6ayZ2sXmpRrm5cxX49szcfl+LGYRtT4Xw4XvsMjkPNDUnfvvbldfvp5eUsNjm6Q0u+eF+vN9wlrjERDDpX1LaNJhBJqMfbzxyhO4ubnx2CMPcd/oESz9dgUZmQlk63KpVCh4+uX5KKUWHpo0lpDgQErM/vj42ka/JBIpAe3u5/V5i9mz40e7+9q5fQs7Pn+H17oEIZO6AsGU1xqYtOIUF3efQRAE5s9/53flkwmCwLrvv+bBh54gNcmIb1RnzCY9RddPIVe50u7uGSTv/wqPgBgCY7sjCAKC0hXXiOa8v/QTfHx8UCga/3zd3Nz4eMWPzHnmUVyNBQQ5S8mstiD1CWPpmiVIJH8qaOvAAQcPHvzDx48f/5+PDGo0GjZv3kzXrl1ZtGgRVVVVzJ8/n8cff5yVK1c6qv3+gfwpQRMSEkJaWhobN260+2d9s3FlSEjIn7mEDW+88QZt27Zl2LB/5rfIh6dNpVekBzMG3UpobeLrShNfFyJ9XJi58SwGsxUXpRRBEOga5ctdLYNYcTKNV4bE4ePihMli5cezmby/O4mZg+Nsxs8s0+DmJCNXhKndovn8UIrdHHxcnMgsr210jtU6o021k8liRSoIZJZpuJBTwdzhbZnh7cLz2xZwUatG5uSKXlMBohmroQaZwhWprOEXqsrNF4vZQGVRGn1i/RpswimRCDzXN4Y5x4yoNWmsnGi/NAbwwb4rBPR6wW67TKFC7eaLW/UxNm5ehru7O0VFRbi7u+Pt7c2M555i6IhxGH164RMbAdQJsiXrD6ErPEdg58cbnLtEKqPGqKS0tNQmRC2KIss/XcS8HsE2/wC9nJUsGd+eny0xvPPBJzZjVVZWsnbVCsqKCujcqy/9Bgy0OdfDw4OtG79n1NiJpBakonByIabLuPqGnq0HPUlZbjLphz6iZ/cuzHx2FH379vnd/4BDQkJYtmE7hYWFFBYW1ufzOHDwV1BTU4NUKrXJmWkMqVRKTU3Nf2BW9sjlctRqNYsWLap3+XZ3d2fs2LFcunSJ1q1b/1fm5eDf508JmoEDB3L9+nWWLVtGVlYW3bp1QxAEjh8/zv79+xEEgUGDBv0lE928eTNnz55l27Ztf+i8X/Ni+KuTlX+L4z9vYdXUHjbb0kpqKKrRM7lrNI/3aY7ZYmXPlXx2XMolpbCaCq2RpZO61UdN5FIJ93eOYtGeZI6nFdcvU10rruaTA1cJ9VTTO9Yfd5WCap2JWoMZZ+Wtj1khk5BbriGlqIqm/u52c1xxMp2RbcPqf96VnI+fqxPH00u4q1Uwvq4qfFycWDGhBWaLlXKtAY1OzrGsanZYhnLt7NZfqWjKQ65UU5lznj6x3o0+p/ZhXlh3nETV+WH+tepr5t4TR/SNflMVWgOL9lwmuVJGnF/DfcHcA2I4enwVUx5+ktJKPaLcHdGkISbMh769u1Lr1Awvn4j64wVBwDu6DwmXDhDSSHk5gEWQo9FobARNXl4eIU6WBsVEiKczBacu22z7Yfl37Fj5BfdGu9DCRcnp747y5cJ5fLbyR/z8bJccZ854hmfnLsO3WW+b7YIgIJhqePftOYwaaZsg/VtUVVXx9ecfk3z2BCIC3QcMoVmzf/2hMRw4+DVcXV1/d2TeYrHg6tqw9cCdJiAgAKvVatOyJCambik9NzfXIWj+gfwpQTNt2jS2bdtGbm4uBw4c4MCBA/X7RFEkMDCQqVPtzcz+HTZs2EBZWZld3szrr7/ON998w44dO/6S69xJlDIJHr9Y7vn84FW+eqAbTnJpXXIwMKRVCCGezmw+l02Qh9pmCegmk7s2YdK3Rwj1csZotlJcoyfQ3ZmDKYUMa10XFXu0Vyyvbj3Hc/1b1JvnvfbzdfKVMbz4cwFPdtYzINYPiUSgRm9ixck0nJUy4oLrlguPXC/iXE45o9qFsmBkPMfSiknIKiXsxlgyqQQ/VxV+rirePFSCX79u6PW1pJ3ZRNPuthVHoihy9egqWvb5FxWFKRRUX2/0OZXXGhAtRpQZ28jBn0fWnMdXLaCUy6kwySiT+CGo5IiitcGKoOqCFJCpKJW2wKfdrX9KBdXFvP72ItoMf6Phz8c9mOriNNz8mjS4vzjjPLW1ttEtQRDgV1IFbk8juHr1Kod++IK5fUPrt430VNNTo2fG9Cms2PSTzbkWs4nS9BPg5INvxK2KpeqiVDytGYwYPq/xCzdAQUEBT0wcyZTmLoyMd0cURU4krufBrRv59setjkRIB38Jffr0YdGiRX/o+P8GHTp0YMWKFej1+nqzttTUVADCw8P/K3Ny8Of4U4LGxcWFH374gVmzZnHkyBGbfe3bt+fdd9/9y/5JLly4EL1eb7Nt0KBBPP300wwdOvQvucadRm8RySmvrRcXVwoqaRXsSYXWwFdHrmEwW1DKpNToTQxsHkhupZaOEQ1HMtQKGT1j/Jk1pO6FbbZYeWbdGbQGEwdTi5jeqynh3i68clccq0+nU6IxkFlhpDp4EG3uHoHVYmbZtcN8u/EkMouOyrJ8Qt0VDGkVwraLOZzJLMXX1Yl1D/dm/s+XSMqvpEuELy9sOMvglsF2y0A1VjkegoTw1oM5vWU+SQeWEtNpLEpnD2orC7h6ZCUqV19cvENQewTw445NjGrb8HLk1ycy6RThy/wbDrhDlmqoDeuJb9u78ZQpiLCYOL15HjlJ+wmLG2BzrlGvoSI7EXe/pviE2X7DUrv5IcqcG12a8QlrTVHSVlz7PmMnlKrzL9MnCD5/fx6fLVtdv/3kyZPsTkyhoKAAQYC7WgbXR82yyjSENL21vLj0o/d5qJ19mwUfFyf8rQWkpaXRpEkTNBoN/Xp0x0vQ8kK8D3uu/cipsz8icfIgLMSf3t3ieePVlUiljXcWb4g3ZjzNrC4+eDnXRaEEQaBbhDc+qhrem/sac99b/IfGc+CgIQICAujVqxdHjx791UjNTSO7/4bJHsD48eP5/vvveeGFF3jmmWeoqanhjTfeoHPnzrRs2bA1hYO/N3/aWM/X15elS5eSnZ3N1atXkUqlNGnSBDc3N86dO0dKSspfsrRzexXV7Xh7exMcbN99+O/IIzNe5f3vFvHRfZ0QBIGCKh2eajkLfk7i9Xva1L9oRFFk5al0DCYTR9OK+VePWLuxdl/Op2f0rWcik0pYOLo9b/90kUqtkXd3JTFzUCt8XZ14pn8LtEYzg75OIv5GOwGJVIZvs37QrK4CoOSneQxsbuV0ZglxQZ68NDiufqlqVLswHl93HrmTMxEeCiZ/d5TpvZrSt2kAWqOZ5SfTKCzVEXIjYtJpxCsUZSRydtu7iFYLCBKc3QOwmHRoKvJx8QxC6x3Pwr0pvNA/1kZgHLpewq6rRXww8pYQ2PlwWz46cJbV644gyp0xG7W08TSSn/oTqZW5hLUbjtzJlcqss+hTt+MklxLcok+Dn4FvZGdKMs7iF2XfUVyqSWfOi9OZ8/pcwjrdj3tALCaDlvIru/CpSmD28GjmncwF6io5pk0YQ2VOKg90CmdEmzCsVpG1CZmcyylnXIcIZm5MQOaSRu/BdzNw4CCqSovxCWnYtruJm1AvaAb368eEZi6MiKsTdINb1FWqrUnMY395Fe+89VqDY9yksrKSpKQkvLy8aN68zgHZYDBgLM3Fq4V9485Yf1dWHT37q2M6cPBHeOWVV+p9aBoSNVKpFF9fX1555ZX/wuzq8PLy4vvvv2f+/Pncd999KBQKBgwY8F+dk4M/x1/S+gAgLCyMsLBbuRcnTpzgiSeeQCKRcPny5V858/8Pzz33HDs3rGHK8mM81C2akho9P1/O4+3h8fViBuq+OT/YpQkHUwqJ8Hbmu+PXbZpDXi2s4sj1Yt4fZWucplLIEASBz+7vwsz1Zxnz1UHCvZ0xmKzkVmhRuTZp1LTNMyCa7Zf2E+ShYlzHutwUndHMU2tOAzC1c1005fD1Usr1VpYUtGD+3m2YLSaqrGqc5DLykvcR0mogAP6R8fhHxiOKIme3zqdV/0cw6qrIOPE9oq4cs9yFA66hHF5ylE4RPjgrpJzKLCOvViCo2+MsuHwFzzNXWXxPFGqFjGf6NuWZvvDwhmsk5lnRBfcnuvlgdNUlXDv1I/rS64xsqiKsuTPvH63FYtI3eJ9+kfEkbn0bF+9w1O63BGFF1hm6t49h5Ijh7P12Eb4VP3Ip1YCzTODhNl60DYlBEASsIrz64rOUXDrB4y1d8O7cgUt5lczckMDETpE82KUJ83de5NWt5/hkfCdUchmvvvgwOU/MwtXTm7LaUryd7fN0MjQibd3cuO/+qWSWGllpUbPmcjqDopRM6RCEIAiMjw9my4oEu8Tkm5hMJua88DRl1y7Q0lNCuUEktVbGS28vJLJJNC7yxpOG5TgM9Rz8dfj4+LB8+fIGnYIBevTo8R91CoY6V/tfEhERwZIlS/5jc3BwZ/nLBE1j3Mnelykp9lU8f3d2Hz3FjGee5KONq+nexA+TRbQp1b6de9uGUlyjJ9hDzYsbElArpGgMZrRGMx/e18mmA/dNbj7v98Z0QBRFHl5+jOvFVYT5ulGmbbyawKopplWQB4Nb3op2Pf7DSYa2DCbS141rxdX4uChZcn8TNp7LYlniLoL6Pk/qiTX0GvkqFouFM5vfQldbSWS7e5ApVNRWFJB2diOi1UJl6l5cy84wp6M7we5BpJZqWZeUQ4pBya5SP2RKNZG9HiXe68YyVFR7astzeXnnV3w8oi5SUaM3kVZlJbrrBPyb1EVYXH3CiOs/HVG0cmDHmxhT8mk56HmunVhb3yzzdgpSj2G1WgnlAlcS80HqhEzUce/d/Zjx3FOkpqaSW6XnpXuiUf6i7L2gSkt5rZHeZReY2vPWcln/Zir6xAbw6tZz+LupeLhnLDPWn8XPtS4xetHo9jz43ht8tnYH3855jOe72y61VWqNZOideO7luTjHjiJ+5L31n+X+tKNk7dnHG4PqzPSa+7uxf/9+7rvP3nV59nNP0slynfget6wIjGYrb8x8nEUrN1NqaniJymSxYlY68mcc/LX4+PiwaNGiv00vJwf/+9xxQePAlheefBTF9WNsfLQvAC9vbNwVVimTcuzGktOgG0sFoijy4oYElLIGkmF1RhS3vYQFQaBjlC8WARRSCbKaCirzkvAIbmVznkFbibdYTLCnGssN6/vL+RWIIhxPL8UsQutgT/KrtLyyOZFhrUMIVZup0lbXmb8JEpL3f06zng8iWi2kHPseq9WMxWRErlTjrlbSX3mBaaNumfHF+rtxdwt/5u64yI6rSbSdvASJ1PbX0dkrhHxJMPmVWnxcnJi5Iw2T3Av/qI78EkGQ4Nl6NNqqIlQuXlgtJtITthLRdigSqQxRFCnOSEBTnoezixf6/CvEKk2YRWgS15677xrAlNH34G+tYGS0M7M3n+P1e9rg6lRXAVFYreO9k6W4yCT0a2Jf4iyVCDzdrzkrT6bx/ICWGMwWm32TOoazbeN62o+YzFsbljM61qWueiyjnJ2ZOqI79ae4OhKVq+9t9yTgFd2TyydSyK2oJcTTmWq9CU9PT04cP8b5s6cJDAlj6D3DqK6upjLtIvE9bJeUFDIJ09t68cUH79F32Bi2ndzIsOa234q/TSjkwcd+fRnLgYN/l4CAgP+Kz4yD/384BM1/kJqaGhL3bOabB2+50Lqp5JTVGhpchtiZlIeXs5Kk/ApaBdVVHgmCQJ+m/ry14yKzh7aur4DSGEzM3XGRp/o2sxmjqEbPx+M68/WRVDzUOjLPL6ekejBeMb2RSGVUZJ/DcGUDnw8P59WtFxncoi5Cs/JkOoIAb41oi4uy7qXeMsiDAc0Cefuni7QL9WLFiWWY5e6U5VzCK6QFnjfcg72Cbs3h8uHl+EkqmdbFdl437+XVoa05n3eUkxtep8uYuUgktlEEeUhnZv20giqrMy5x45Ek7280qdfdvwl5Vw/h4h2KXKnGxTuUS/u+RCKVYTWb8AxqhqvMhEAFKXlSBJkSJ3MFTVXHefjedbzQryltQ+uiG7H+7izam4zRbKVIY6JFlz48NutpEr+Z2+jnG+Cmokpn4si1Ipr9oiS+W5Qvrxzcy+tHT+Pp68e8OS8SoobWQe4MiHDh05/20npEw2O7Nh3C+kvf8Vg3J64UV7N00TzauJlp7aukIMHExE/fo+PA4bT1bng5MdzbhcJz13jr/Q9ZVFXJm4d30cFbwCTC2VKRofdPY+BdQxq9LwcOHDj4J+AQNP8BLBYLO7ZtZc13S4n2c6NGb6r/5j+xUxTv7Upi7vC2NkscP13KJaWoiuVTerD8ZBobErNpHuBOYbWO3IpaNAYTE74+RLi3C3qThYIqLaPjwwn3vrV0UFClRRTB1UlOv+aBnMkoRWes4G6vRLafOIzRCkPD1YwaG83VwiryhCD+teEagyOduFZSzSM9Y+vFzE0EQeDJvs2Ys+UcXoFNMSncyTi/g/ghzzd4784eAdylzgTqloyWHk3lUGoRUglIEXi8bzNaB7mhl7Tm+qn1xHYdV39ufupxShJ/INhZpJ2fgvJryyiv0VFTkoarr31pta66mPimfvToqOLjM9V4BTXDL6Jd/f7itNMUXcsm9q5XUarrBIcoWtmesJZhLSWsPp1O29C66EsTX1feuKctUNcWYIfJjTmvzeWeAMOvftZWq8hnB6/y7eRuNtsNZisyhZyysjJ++GQBKya0synHX59ibHRMmUJFhc7M4+su4O3mzCsdXOtNCVsC/aJFXt25jmDXhv+czRYroqQuv2rG7NfRPvciJ0+eRC6X81SXLjY+HA4cOHDwT8UhaO4wOTk5PDNlPP2CpDzZzIW8iroIx10tg+jbNJBwbxemdG3Co9+fIMbXjQB3FalF1TQNcOebB7vx9s5LvDsyHrNVJKNMQ49oP/anFOCpVhLj587Co4WUy1wRvQW+PVeAUpbNiDahHL5WyObzOcwd3hYAq1Vg37VS3hjaks8OpTA2PpyuUb5oDCa+P53OuoQsCi3uKJ29OOQ2lmrxK3rFNLzO7alWojVa8G7bHzf/JhxfO7t+X2XuJbSpP+Eu1WOxgrHGiLSVgtyKWh7/4SSR3q68dndrfFycSMgq47ODKRjNFpxa+5J3+QBQJ2jSErZgyT7KRyNiaRN8K9phMFt4dvNX1LR6GFe/aJt5abOPsP7HL/H19aVzx3bcN+VZWg18CifnuuhWXuJ6Yvo/Uy9moG6pKrDDBLbveYuOXirSS2qI8rU1+gr2VLPtu12omw7jaN4BHmzksy6vNXApv4K3R7TD1cnWb2jzhRwmP/4q3335KQ+29LDzFpKZqjHqNSic7HNZiq4dIz2jmCHDRxJZccHOYVkiEXiySxDPb0lmepdwu7F3pZYydOwT9T+r1epG+9s4cODAwT+VPyRoEhMTf/exNw2K/r8z87GpvNbVC7cbL6EANxXxYd7M2XKOVkGe+Lo6EerlTKinM//qGUOtwcKkLk2Q32g/MLFTJFsv5jCuQyStgz2xWEUu5lbwWO9mPL+3kqC+swmW15UCW8xGFm9fwIHUMwxuEcT7ozugkEkortEx97gGVfQw9qWd571R8exKzmf+z5eQSwTSSmr4cmIX1pzNJsFtIN4hrSgQpFTqjATI7R1/RVGkUmtElnEQ3fVdKEQduVcO4mSqoJvyGg8PD0Z1w6emsFrH2zsusOpkGuM6RvJA51uRlXBvF+6OC+GhFccoyjqFRFr3jIy6Gqrzkni6vY+NmIG6vKKZvcN48PsFuIR3xb95X5w9g8hNWINaUsv4KU+BaGFwv+5s+eELHn5iBuXVdVEVKxJcvBou8Rf84vBXnSO91F7QAJQbZEQ160thVRYnMyvoEuFp90wW701m8diOxPjZNrYrrzWwL72S+WPG8vDEMYxsbrs/s0yDylJN0anvCOn1hE0lmr62AjexgAvZuSx4Yw7tnRruMh/oriYsLJS3DuXwWHtf/N1UWKwiu1NLSRKCeGK0fRKxAwcOHPwv8YcEzf333+9o2PUHSEpKoolCh5vK9qUsCAIPdY9h47kspvdqypnMMnpE+9dVxfziXdopwodtNwRNjd7EB/suU6Uz8f7eyyhEL6oyT+Id0wcAqUxBm6EvYj7xDvfE3aqkWXqqCO9OD+PsEciJa84c2XCAKHeBWoszV7NLKDWpeOSHBOQSAVWLKixmE1q5N9+dyuOVgbZREIDE7DKCPZ2Zf5cKF6Urx9NF5u38gR7NQ3m6l20X7gA3FQ92jmLh3itM7BRlN5ZSLuWlwa14YUMiHoKU9DXTqNSZqDGJfH9Cys4L11k0tiMeaieMZitP/HASZ6WMt4bEIJdWsP3Sx1wsrEH0a4dHlwdRqNwQRZFtCVfYs38BC+bO5sdN2wHYuXNXo009QaSoRk+TBsSM1SqiUNcJGL+Ok3j/8Cd0y8piSocAvNQKLuZV8l1iIXkVFo5cLybIXY2zUobFKnIwtZAlxzNZtWM/mZmZePn4k1V+nYjblgaXHE5h3oh2XCzU8OXuucjCeyJR+2EuukBJRgI7d+9AEAR8/AMoLjTUOzXfjtFsxd3Ll1nzF/P5wneouJaLVZBx16iH+WLCREfTSQcOHPzP84eXnO5kGfb/GhkZGUS6NPy8onxcyK/SAXUvTFMjjpqiCBdyynloxTGqdSYWjulQ/zIURZHvzpxgz7kS/NqNBeq+0V9Ly2DkF7ko5RKqtSb0KGnVLoCya4cxZx3FVSkhvdqK1actZt9SInVpjGvlS3yIB9dLk1m5/xj+wfEcyDxJq+Qi7mnhVy8CrhVXs+RwKh+N61QfhenexI/2oZ482iOiwXvYm1JIyyD3RsVw6xAvJBI5Emdv9AoPrHIt0XItT3QNoFpv5IkfTqOQSQCBh3vE1DvxAnSP9uNaURXPbkpCoaqLfAiCgFzlScLhK8xcuJHg5n0AEb+mRhK2vUfbu5626TUliiLW4kuc0VRRoFfy7UUtzlILU9p50S7Uk+yKWiymus9KIpES0udZkkozeXT/LkRjKTWaGpZ8+Sl9+/Zl1coVPLl4PhKzAZMIvYaO5KGZ0/jXEy9jUfpiqK0iqeoan4y9ldtTWK3D19WJ/q5O9GnizcmMc5TWmmkV78p5Xy8uX75M8+bNGXv/JJ7etIoOYfZVVj+llDB8wgtERUWx8POvG3zODhw4cPC/zB8SNE8++eSdmsf/JE2bNuVoNfRtYN/lgioSs8uY/N1RCqu1BLmruTsu1O64Tw9coVOkL8EeatqFedt8sxcEgYc6BXN+yyVM+iHoNOUU7ZrDCwOaMTQuBLlUQmaZhvd2JXHlh0eY1i2KsWNCkUqEOo+T1DQ+T0th1dTuKOV1CclRvq4MaOrPIz8c4qxWxtdpXvyQlIGn0kKtCaorS1jxYMd6MXNrLtglEN9ELpWgNzVugb7mXAGS8N5Ed5uIcKPKSVdTwhfHPuSbUVEMbx3K69vOc72o2kbM3CTG351hLXzYm7yP4Jb9ybtyiKxLu2kz+GlcPG+VMYe1GYpXWFsuH15G6wGPASBaLRSe/QE3QUOKXolX/AycVW6YDFreubiJDtfTKLHIiAr3R1OeV79k5eYTgdrtQQzaKsoPfVafkzLpwclMenBy/TW/+OpbVmxLxKftrZ5m5deCeXTtFp7rHUWolzNaowW9yYKTXIpUItD9tnvcejGXUJMJqOvC3W/sVD7euoyp7fxwdZJjsYrsSi3luiKM50bc2+gzduDAgYP/dRyC5g4SExPD8fRSRjWry5W5icUqsvRIKium9sBTraSoWsfEb47wzbFrTO0aXW+Ydymvggt5FXw9qRuzt5xjajf75R+Aca3c+TTzLCVnVzBrUEv6NbtlrBbh7cL0nrHsvVrA+Phb+SOCIFBZq+eFfs3qxcxNJBKB14a04IXjcvJKSygyibh6hVJdns2kpt4NCheztfHI3T1xIby29TxWq2hnBmiyWPk+WUfsUNtUW5WrL6b4h1h25gee6hnG7KGteeBb235htzO2fTg71u6k2q8JFYXX8PBvYiNmbuLiGYTZoCFx46s4q12w6sqRWI1kaQ0IzoGc++kDJFI5JqOWyLZDOVRs5NN5T9KhQwfadehBUPw4VJ4hXD+1DlEEmVyBu28gM2e9zvy3XrPpr2SxWFi5dquNmAHwiumDLiCOOYc/pleXUJSe/vyYkMmkLraVW7UGM4m5lbzcs2f9timPPMqZtu344uOFGDWVWCVyho55hMfHO5aDHTj4JZWVlSxevJiDBw+i0Who2rQpL7zwAh06dADgypUrzJs3j6SkJDw8PJg0aRLTpk37L8/awb+Lo8rpDrJ5w4/0Dndhwa4k2oV60j7Mm7xKLdsv5VKlN+JxI1HY303Foz1j2XAui6uFVajkUoxmK7UGM//qUWe5L5UIjb6wVHIJVrMOb5VA36b2lUk7kvJ4ok9Tu+3JBZUMb2MfFQII83JGbiig4/CXydv9Fu/0MCKKAey9mt/g8YHuKjJKa4j0sc9BaR7ogc5k5qP9l3lugG3Tt1MZJUgC29udA+Dm14TE5LrIjlImxdfFCavV2mA+iFouRbSayUneh19EPEZdVYNjAniHxlGckUh5jRZBlCAVBcxSF9r2mYrLDadik0HLpX1LcPUO44cftxIfH0/LCB+uH1mMySKiiuxNVLcHkUjq/oROZl7m4cee5duvPqm/TkpKCqLKXlQBqFy9qfZqwsLPvyYp6RLP3j8crcnChA4RuDrJScwp59tj1wiOaWXXq6xjp850XPVjo/fnwMHfhatXr7J+/XoOHDiAVqtFrVbTt29fxowZQ7Nm9t5UfzXPP/88ZWVlLF68GC8vL1avXs20adPYuHEjXl5eTJ06lQEDBvDmm29y/vx53nzzTTw8PBg9evQdn5uDvx6HoLmDfPPReyweGI60cwQXcis4l1OOr6sT743qwKbzWZzNKqNjRJ1r68X8Cip0BqzlMKh5IBM6RrFg16X6ihlfFyeyyjQ2PjM3WX++gNwKIy2c5A2KHr3JjItSzqpTaVzMrcDphmAq0xooqdET5GHfekFvsmAWZJRnn8NqNvD+3iv4qOXkVtTycPcYFL+I6ozvEMnszYl8OqHLjXyXW+RXahEEgV3J+VzIrWBM+3D8XVWczChh64UcXNs23tlW5PdFHQ6lFiLxjcNqNOHsEUh1SXqjxxp1NbToPRVnj7pI1uGVzxN/99P1YgZArlTT7q6nObH+Nc5UShjULpbpvWLo1asPVqvIT0l5LN/wGC7dnsc7uDnugS24fOky2dnZ9T3NJBJJXRLUr92dINCmTVvuf+JFvnl/LteLqrl5yxVaI6Xp1zAajSgUil8Zx4GDvxdms5n333+fDRs22PRwqqqqYtu2bWzevJnRo0fz4osvIpPdmddQVlYWx44d44cffiA+Ph6A2bNnc/jwYbZv346TkxMKhYI33ngDmUxGkyZNyMrKYunSpQ5B8w/FIWjuINryEmTScADahnrVm7YBNPF1I7eilmitgdMZpVzKr+Tx3s2JD/PienENU1cco1proKAyHB8XJ8Z3jODdn5OYd288TreJiYSsMi6WCbQf/iI566ZhMFnslpCC3FXM3HCWUe3CbcqmD6YWsGhPMt4uTmgMJmQSAZPFyvA2YaSUGjCYZfSv3c6UcU1QyqVYrSJ7ruYzadlRlk/ugZPi1nWul2rJNrox8dsjTOkaRY9of3QmC5vPZ7PnSj6Lx3bk3V2XyHfpxPuHzyExVuOlkqKQybHkJ0DroXbPr6Y0k9Y+dW93o9lKbkUt5VojPi62Hav1JgtLj6Whd5eh1xpQewSgKc/DYjYhldkuj1nMJmor8uvFDIBC5WojZm4iSKR4h7RClrebVdN63xJqUhjZLowBzQOZtHwx7iM+QaZwQu7bhk1bdvDMU3X5ObGxsUj0+Q1WVlnMRvw8nOqjTVvXr+Opfs3IrdBSpTPRNtSL9mFenEwvZfJ9I/lh8w67+Tlw8Hfl/fffZ+PGjQB23bZv/rxx40YEQeDll1++I3Pw9PTkq6++olWrW61eBKEuf7CqqoqkpCQ6duxoI6i6dOnCkiVLKCsrw9vb+47My8GdwyFo7hAWiwWd0YTRbLWLWACcyy5jd3IePyZkojWa+X5aL9Q3Em1DPJ3pFePPQyuO8d2J6ywe2wk/VxWP9W7Ka1vPEeyhxtfViVMZpZRq9ER7uJL985tUmmR8c+waj/exDeW6OymID/Ome7RtQm2QuzPlWgOvDImrFwkGs4WvjqSy/lwe4zuEM737rTJsiURgcItgPNRKRn93hk5RAbg7STmTXojJrwPOsS2Y6JZAQlYpa85kIggCA5sH8uPDfZBIBHxdnKjwDiOm9zTyD7zPYy2tvH9OgqU6g6Lzm/Brc2/9i9+graL6zDdMGxWOKIos3JNEtUnCtJXH+Vf3GO5qGYxUEDh6vZhPDl6lc5Q/49q7sPREORlZ54iMH0bSvi9p1mMSUrkTWSdXUVlwFUQrollP6oEviO49HYlEgkTWePRDrlQztWt0g5+hq5Ocx3tG8emplUT2fBjRakF2m9uzRCLh8X89wEffbcGv1fB6fxmrxUTRue9Z8sGt/kk1BRl07d/Z7hpdm/jy5fFTjc7vr6Sx5TwHDv4IV69eZcOGDb95nCiKrF+/nnvvvfeOLD+5ubnRu3dvm207d+4kOzubHj168MEHHxAbG2uz38+v7n9kfn6+Q9D8A3EImjuERCLBydWNb49d49HetvkrtQYzOy7l0jvWnxg/N0wWa72YuUlRjY4wLzXnsiv4+mgq07rHEO3nxnujO5BfqWVXch5Gk5nyWiM6YyWCUImPk8Cm89lklGq4v1Mk3i5OHLlWxNqzGfw4vY/dHD/Zf4Uv7+9qE9FRyqQ81bc5R68XM7WrvW8MQOcIH7w8K8lv+iQXss5h8KvAJ6w1ZSe/JLh/CPe2aWN3jtUqkl+lw6VpBABSzyYoZelIJCLK8O6UXdmJJucMRoU3Om0NfjINz3Tz41RGCd+fysDq4k12STmpqam8PvtlVq46i1QioNcb+Pr+zni7OCGKIh5yK8VnVxLa9zliutxHyom1aAsuEtqyP21GvoVMoUJXXULumdUkrnqMVqPfxWzUYTJokSvtl94qshIY0Dey0c+5f7MgPj1aJzhMJecZM+pDm/0TJ4xFrXLio8+/RWd1QrRa8HQW+Pz92XRoXxcGF0URF0XDnbCBX933Z6mtreXlOXNJuJCKWZSgkFi4a0B3Zr30gkPcOPi3WL9+vc0y068hlUrZsGEDs2fP/s1j/ywJCQnMmjWL/v37069fP+bPn2+3lKtU1vXUMxh+vcWJg78nDkFzhxAEgdJKDTlSOYv2JDO+QyTeLkpOZpSwMTELJ3mdcFhzNoOO4be6H4uiyPS1F0jTu+HdZChObiaWJR9m08Uj3NXcF6u1zthOrZBSrDHQJcqXMfHhCAJsTKzLy8koqeG1recQETBaLKidnG36RNXoTVwpqCTIU223PHWTcC9nm6WtX+Ii6Lhy+DsslZm4So1cu7KTL+9rxaK9KXz1gKfN9QDWnM2gRCvSxK9JnXGfTsv7ey8T4uFOmWtHmo79jKLLu5GXpaBWB3P98hHe2lVBUHgUPxxMICQkhPPnEvl4/hvo87Lwd1GSUVLJx2Pa4X0jurQhMYsWQR483ieAdw98y5UqBeaiPJr2mopneIf6uajcfInp/wwZR5aSsv45AlqN5NLeL2g35Nn6snGA/JSjKA3FKGUNV5dBXSdtiWiiIusU3dtF4e/vb3fMyHuHMfLeYdTU1CCVSlGrbYWTIAiU6y0NLk1ZrSKVBmuj1/8zGAwGho2aiBg0AO92t3pP7bp4ictTprN6xdI7cl0H/9scOHDgd4kZqItkHzhw4I4Lmr179zJjxgzatGnD4sWLAXBycsJotO2hdlPI/PJv1ME/A4eguYPILXreHtGJ7PJaNp7PokZvIi7Yk4VjOrL7ch4n0ksI9nDmWnFd7yaAN366TKlHJ1p1Gl8/TmDcUFL3fYxcWszwdqFYRStHrhfzZJ9mNiXaLe724Mi1Ij7cf5lobzc6Rfpyb5tQxnx9jOIaHWaLyOeHUlDKJOiMFtpHNB5SlQgCFqto1xfoJlXVlawaFUWIZ0esVpF9qcXM3HQRZfRdPLjuAg+09qRTmBslGj1fn8ghraicLpF+JO14EYWo5ZkuETTr1omMshpWnFzH1aQtKEM6YRDVVOWm8tyMF5j7xq0lmSOHDvDxnGexaCpo6u9Ox9YhVGh9WbwnGZ3RQr9m/hxLK+WzCZ0RBIE3B0dRVqNnzNoaGzFzO8EdJ1CefR5J+nbujvJj84+z8Ahti0LlSlnWOWIi/DDKLJTXGvBqoBs6wOX8Sqp0Fqb1DuXpJx9t/JcBcHW1rwC7SZtufdhyIZV729pWnX1/JoO+99yZBMVvvluB0aM9nl621/QIiiP9ah6nT5+hU6eOd+TaDv530Wq1f+j42traOzSTOlatWsW8efMYOHAgCxcurI/KBAQEUFxcbHPszZ8b+mLi4O+PQ9DcQVyUdR2Ow71deKpvc5t9gR5qsstqGdY6hMnLjjKoRTAKmYTDuWbiRo+1OVYQBKJ6TGPT9tk82kuNxmDCWSmzETM36RnjzzfHrmE2W5nQMZKsMg0Wi5VPD1ylSmdi7vC2uKsU5FVo2XAuq9G5R3q7sDMpl3ta25d1Z5fXojeZCPGss+CXSAQGNvOnhb8rz+zPxW/QW6xJP8WqY6kITv7IYvpjLvqI4spqQtVSFo7pUh+JCPZU072JH2//nEy2OZXhEycw/clNODndSvwVRZEP5s7CWFnGm8Pb4qm+JS76NQ1k8/ls9l0toEJrQBTrTP4AzueW4+zRcNk0gMLJBbnKjXtbyGkf7s3M/u48suoAw9uEE93cjW8vl9N38nQ+3fcDr91tv4wmiiIfHrhKdHz3+kTgf5fPv13JvQN6cXzTOe5tHYxVFFl/Phd1SFPWLPrwT43dGNt/PoBHVMM9ntwjevDl1yscgsbBH0atVlNV1bhtwi9xdm64P9lfwerVq3nrrbeYNGkSs2bNsllG7dixI2vWrMFisdT7R504cYLIyEhH/sw/FMci+R2kSm9BZzQ3uO98TjnNAtw4mlZM10hfHvzuCAdTCpEq3WyWPW4id3LBQJ1df0WtiSD3xkOiYd7OCFKBap2RcWvSCPTzQ2u08FTfZvWdmoM91eRXatEYTA2OkVRQwYcHUjiRXmazPbu8lqd/TKRLpC8f77tssy/YU020UxVGbRXeUZ0xqoPJzUxGeWkp6x7qQqC7mllDW9stqxjNVgY388fd3Z2nX5hhI2YADh86iLO+nLHtI2zEzE3ubRuGWiFjWvcYlp9Mq99+OrcGk7Hxb4uiKGIxG+kc6UNSXgVGi0iolyvD24TSIsiTOd18ybp8joQiA+/tSrJ5VmW1BmZuOkcOQcx47vFGr/F7EQSBLfuO8N66vZx3a0uSV0c+23qENdt2/emxG8NipVFvI5lCRW3tH/um7cABQN++fW0MJn8NqVRK374Nean/eTIyMnjnnXcYOHAg06dPp6ysjJKSEkpKSqipqWH06NFoNBpmz57N9evX2bhxI8uXL2f69Ol3ZD4O7jyOCM0dpEWnXnxyMImZg1rZbC/V6Nl7pYBp3WNYfeYcs+5qza7L+SRklaLVNpxLYTEbsZp0vLntPDkVGtwbeLHfpLhaj5tSxus/XyOq2yRKziwl0E9O9C+6QD/epxmvbjnPM/2b17dUqNGb+HD/ZfIrdbiHtOOjjBA+O5uEu8KKziygUYWhaDqCTsEprD+bTpXWYDOXu2NdWZCwAWlJIp0ifHCPtFJQKfD10WvoTBa8b1u6EUWRj4/mcrRQitS/NVptNT0HjODNOc/Tv2+f+uNSkpOo0mhtWgL8kvgwL3xdnfg5Oa9+qSxbI6G2qgSzSY9M7mR3TlXBFZTmKmoNJo6nFXMpr4LesbeMCT3UCjxNBazauJ3hI+/jyPIEfFUCVhHKjDKEiAHEhtUyeNCgRuf1R4mKimLhh5/89oF/ATFRIVypKkLtbh9er8y9yJi7+/xH5uHgf4sxY8awefPm33WsxWK5Y54vu3btwmQysWfPHvbs2WOzb+TIkSxYsICvv/6aefPmMXLkSHx9fZk5cyYjR468I/NxcOdxCJo7SPc+fVj+/kEe/f4E07rF4OtWV3W0ITELk8XCSxsTsFhFErPLCPJQ8cKgVuy9fpSKtON4RXe3Gavk0lbGtXLDWy0jpaiaco2e9JJqonxtRUpOeS3F1XrG9GnKW4fLad0zjvzz/pRri+zmF+blzOyhcXx1JJVLeRUoZVJKNXq6RPoye0gc7+e0wK/FYGAsoijiKgi4ApqKfC7kn2daj2i+OX6d529z/z2fW05baSFvP9zFRpRdLqjk/d1JNtf//HgeZxU9Cerfq36baLXw4usf8X2AP82b1y3TqV3dyKvU2rVNuB2FVIrJIuLtrKS4WkeghxqlVMA3siMpuxfT/K4XkUhvedLoasq4dugrmrlK+OzgVV6+Kw6D2crWCznkVmj5V4+6cvX2PlJSrl7h7KmjPD/zVVKzihGkatwttXTtGMj8t179x1YDvTzjaUZMeBynDlOQ3BYVNBlqMRedYuL9s/6Ls3PwT6VZs2aMHj2ajRs3/mozY0EQGD169B1zDH700Ud59NFfz2tr3bo1a9euvSPXd/CfxyFo7iAdO3Zih6uKLpHeZJZruJhfQVyQJ5se68v6xCy2nM8BRDLKqnFXKRBFkWY+cpzL95B64irO0f3qKoKu7aKXTzUDYvz4YE8yrw5pxVM/nuX5H8/yxG2JwQdTC/n0wFWq9QbahXkhUFbnVKvyJteg46N9yexLKUS0QpCHig/HdsLHxQkXhQxvF2Wdqa2g4vE+zTBarFgu3cqxuV2cGMqziPJUEOXjysqTtxx5RVHkeFoJq6f1tIswtQj0wFVZ5zQc4umMyWLlUJ5I4IBeNscJEim+rUbx9oIP+X75EgCO7PmJHk38uJRXQVywZ4PPOjGnjMEtg9iZlMs7Oy/RMdKbJm4WcqxqXH0jObdhFt6BMSjd/KkpTqOqJItHO3jyYJd4jlwrYuXJdOYOb0ebEC82n89m3dlM7usQgdEi4iyX4+vry8rvvsRsNlNbW4urq+s/VsjcJCQkhI8XvMyM2e8gusUiUflg1eSiMuWzdsUXDndiB/82L774IoIgNFjCffPn0aNHM2PGjP/iLB38r+EQNHeQtm3bojVZmNTFvux3bPsIdlzKxUOl4EJOBVnltUxdfgyFTMLrvUKo1hvZmfIDSincM8AHH5dQvj56DXe1ko8OpiAVoEpn4PNDV1lyJBUBsIoi7io5tQYTL65PoEeIF6lpp6koycJJLMASHMSX93fFRSnjRHoJDy47SlN/V8xW+HR8FwAyyzS8sf0Cs4fE4WnMQa8px8nllsOx1WJCm7qTweOiSCmqIsBNVb9v28UcesX624mZgiotJRoD07pHM3vzOZZM7EJBtR6pVwwNoVC5UZhVl1RYUlKCUJ7D1G7RvLH9Au+Oao9caiskErJK8XZWopBKqNGb+Hh8J74+dg2TyYR7+TEsTccR0n4sBSlH0NcUInMNopNTOZM61/nLdG/ix86UckasTMHFxR25xYSptpRR7cI4UWzlvb796q8lk8lwd3f/A78Ff2+6d+/Gsf1bOXz4CJlZ2cS1Gkz79g331nLg4Pcik8l4+eWXuffee9mwYQMHDhygtrYWZ2dn+vbte0cjMw7+/+IQNHeQ06dPM7xdRKP7h7YKYf/VAtJKNXiqFcwc1AqpVOCLwym8PLgV07veyv4vrzVwpaCShWM6IAgCacXVPLnmFJ+O78zVomoA2oV64eWs5NUt57CKVqpqqrieuppAaRWfju9sk0MzuGUw/ZsF8vCq4/Rratud+/V72vDJgSssuKs5T29dRFVAJ1SBrakty8acsZe5ff1QyCR8e+wac4a2RmMwsTohl50XM5nS9ZZ423+1gK+OpOKilJOlV2FRBYCqJYO+vUxbHxGdvOEIhyiKCNR5r1y4cIE2nhLcVAqmdotmxvqzjI4Pp1OED1U6I5vPZ3MgpZAVU3owd8cFymuN5FVoScqrZNGNZ7X05E9s2vYjzi7uuKCjX7iChzrG1Auvl35KozBgBFFdO9+wRrdSkLSbJzcdoGWnrnh6NhwV+l9BIpHQp0/v3z7QgYM/SLNmzZg9e/Z/xDjPgQOHoLmD7Nn1M67mhquIAJQyCW4qOdN7teXHG6ZwAO3DvJm5MYG7W4UQ4K7iVEYJF3IreOWuuPqXcBM/N6Z0i+a1ref5ZrJtvs3Y9uG8tu08bUK8cDLk0q1ViF1CMIBMKuHNYe14aPlRJnW51ePJ21kJIjjJpKwYF0tidgarzx0nSGLmzfFxSCUCy45f50q1nIc255NXXoXc2QtBEc1HJ8vwc3dBp9ez4mQa80fG8/zuUmKHz0SmqIvmiKJI2vFVFGYkEtLFgFRum+Bcln2R0QN6AKBSqdDeKBSLC/bkvdHt2ZWcz+K9yagVMoa0DOZsVhmvbDnHqLZhtAr24OHvT2ExW0kv1RDr78aj3ULJKU5k3r323wiT8yvJVrYgIKpL/TZBkBAUdxeX917ni5lzGv38HDhw4MDB3weHoLlDZGVlsWfN10R6OTO6XXiDxxxLK0YQBALcVQTf1vF6UIsgesX48+QPp8iv1DJvZDwPdmlit5Rzb5swlp9I++WwuCjl9Grix6WCSqRSKeM6NG7dH+bljK+rfQWQVCplybEM3FVy9l7Jp0ZnpFeML+/vvcKZzDII7U1Iv0Gc//kD4kfMRuVa53ZstZh4/8S36HIusfahLnx4NA+fro/Wixmoy8dp0u0BSvKvcWHPZzTtNgFnj0BEUaQ4M4Gqa7t5evURADp37synb1q5WXeglEkZ3iaU4W3q/HHO55TR1N+NFwa2rH8+z/VvQUZwDz4+sJfakqvIpQJFVdoGG3f+mFSBR/NJDT6b4LghbNq6gxnPPd3o83PgwIEDB38P/tlZjX9jXnziEV4e2IIQDzUHUwvt9v90KZe0khqa+LrirlJQqrHtHeIklxLkoUKtlNEu1KtBv5DGqn7Wn8vkaHoxz/dvgY+LEoXs1z0hpA0kt2ZopCw9U8zK3EBcB75L2JglnHIZzuZsFaUucYR2mcTlQ9/SeuAT9WIGQCKV499+IqUWNS/9nM2hHFODZcGCIODu3wTRaiX97BbO/fwRZzfNRSg6xvnTB+sTUmUyGTHturKiAeFWpTOy9Mg1pvdqavN8ukR4kZZ0jp8On2R/UibPvvMJcR26sDUp324Mk0VEImlY10ukcgwGo912URRZu24dk6f+i0WLP8BsbthryIGDfyq/Vp3kwMF/mt/7++gQNHeI7NRk4kK8eKRnLMn5lby65Ry7L+ez90o+r209x5IjqbxyVxxltQbUChmiKFKq0duMEe3nRo3eSEphw66bPyfl4e4kt9mWXlLN0WvFfDahC3EhXvSM8WfvFfsX+U2qdEbKfnHdK4VVZJbpiO4wgrh+D6NUuyOVyQlq2p0Ow1/GYjaQeXwZ6Mtx9bZ1Eq4pyyFp/xKa938SRe+5mJ38Gv1lFICm3Sbg5heFu28EOk05i997C5XqVjRHr9eTdOoIIjBrcyK7L+dzOqOUJYdTmLvjAmariIvSVpAIgoBUEElOusTYgT3IWLeIZ5uJHLqaR1G1zubYe5q6UZl2pMH56YvOMWbkMJttx44dwzekGW9+tpM0fSyr9mQTENmW995f1OgzduDgn4JcXvf/5I+2L3Dg4E5y8/fx5u9nYziWnO4Aly5eQK/T1Ru8PdGnGTV6EzM3JNA92pdn+7dg9uZEOkT4sPViDiU1ep7s24y5Oy4yvWcMLYPqklA7hfuw9mw6r249x3eTe+B824u7sFrHl4dTcFfJWPDzJaJ9XTmVWUpKYRXNAzwIdFeTmFXKxdxyymqNjO8YiauT/S/DB3svE+ZRJyBMFis7kov59PA1zF6tCG010O54qUyJs+Y6o2Jr2Wuwz8u5fnoDbQY9WZ8X4xEYQ3neZbxDWtocZzGb0GsrcPEKxsUrGABtdQljH5jOp++9yt7tm0EQULt5MjJaTffIUAwmC8fTiymq0dGvWSDTezXlhzMZJGSV0SHiVpQot6IWd79QXn9qGvN6B9UvM701oh3zd16iaYA7bYI9WXwkn8xqC1pjBpUaDTGdx9ZHeqqLUonyk9K06a1O6bW1tQy/bxqdRr1Zv4TmGdSU4Oa9+OibD2gf35b+/fv/xm+HAwd/X6RSKR4eHvU9jdRqdaNu0g4c3GlEUUSr1VJcXIyHh8dvOlAL4v/j2OLNl8++ffv+0nGnTxyLNi2RXjH+9L+t35LBZOHtnRdxd5IjCAIvDGxJWa2BlzcmMCY+nBYB7szcmECkrysSQcDHRUl8mDfv7bqEIAh0jvAh2t+N8znlXMytoEeUL+E+rvSM8adUoye7vJZN57KJ8XPhfG4l4V7OTOgYyYXccrYn5fHK4FZ0jPBBEASKa3R8sPcyp7Kq8G0xCElNDhYkyMO6k3XlOAZtFd3ue8vu3jJPrWGYazL/6hHDvL0ZpEc+jItnXb8kTXkehWmniO44qv54i9nIxd2fEd52CF5BdUm5+toKUvd+jE/zAQTF3kpoTtr/FU6liTzeM4qeUd4opALPrU/gs/GdGl1ey62oZUdSHtN7xgJ1fwBvH8oloHU3OmsvklWuISG7DKkg0Cc2gO5N/Ji3K4VtWRJiuo7HO7g5Bm0VGee2U5Z1jti4rkgstfToHMf8t19HJrslIidMnEymMQafsDi7eRh11aTuXURG6vk/8JviwMFfz5/9vyaKIoWFhVRWVv6Fs3Lg4N/Hw8ODgICA3xTXjgjNHUBfXsCLA1sx8dvDtArywM/VieSCSjLLahnUPIhlx68hCHWrfd7OSj6b0JnHVp+kqFrP2yPa0TbUy2a8blG+bDiXxaK9yViv+eHfcjBRwcc4m1PES0NaI5UIhHk50ybEi1Wn0jiWVsITfZrVN5bsEOHDsLhQHltzAosVFFIJFVojRA0m7kH7hNj0C/vw8I/EoK1Cqbb1XBFyDzNxcl336ke7BvHI5i9R9JmBQuWGUV+DUm1b4iyVKWg98HGyLu0m6/gyZFIJKmstHXwVVAXE1h9ntVrQFF6lWbAP312GZde0yAwlqEUpBrMFlaLhX1WdyYL8htjJrajluwvl3DPtWVYu+YRLZbmMahfGnCGtMVtFdiXn8djqk5zXeNB59Ov1zsFOLl407/kg2Z5B+DuXsOnHDQ1+Ezh0PIH4RmzRFSo3tCbHN1kH/3wEQSAwMBA/Pz9MpsarNB04+E8gl8t/d28wh6C5Q7g4yfByVvDIihM4O8no2yyA1sGe5FZoMVuhuKaWtJJqmvi6oZBJWTAqnsnfHaN1A064MqmEcR0iWXc2iyqJEn3WcVy9DbQK8kB6W+RCKhFo5u9KWqnWrku2n7uKDdP78eqWc6QG3Y+n1YxRV2N3rcJrx1GpnQlpOYDrpzfQovdUG1XsIqdeXHg7K/n0niAWHFpInkGNBSkltRZCW9o2m5PKlbj5hGFK3UorLxVP923Gi3tLCXDzBUAUrXXRGbkUc4cXCHb2vLFdJO/sWt7cdZ4Fw1o0+Jw3JGZRUKVl5oazVDv58vWajbi5ufHxGzNZMqEjCpnkxjOEEW3D2JlaRUT8vTZtEG4S2qIvZ7a82egfj0wiYLWYkcoaXscVrZYGtztw8E9EKpX+7heJAwd/BxyC5k+i0WhY8skHXDp5BEGA8GZxiGpPNAYTEgSi/V2ZP7J9/Yu1cySMjg/nu+PXmbXpHKPjwxnRJhS1vO6jyK3UEublbH8dgwmTxYKfrIo2IZ5U6KykFmu4lFtOXMitiM7gliFsOJfNhdxymvq74/SLMuXBLYM4cnQ3rYfN4sSPc6jKu0RIm7sRBAn69APEyguY0s6Fr5O34OrbitOb3iIsbiBOLt4UXj+Fk86AyWKtd+sNdFfz0fDous7VVpEBX5yiKvci7iGt669pMmgxXN7Axn91ZUNiFm/suIDW6s61Y6sQJTK0VUVYakto0n0qTs63BJ0gCIR0HM+J9efrWybczuWCSgxmKx+N6wzAi7uzUKvVfLfkM57rG1v/zG8ns9pKeEDDDsWCRIpU4YLZbLZZarrJ49Mf5Id9hwhrNcD+86nIIzTwf9uAz4EDBw7+zjgEzZ+gpqaGqaPv4YFoGSM7eQCQUXqJM/nFzC8BncnMh8M6NfhindotmisFlehNZh5bfZIoX1dEUeSLQ1eZP9Leev67Y9eJC/bk7RHt6iMmJouVWZsTmSaT0izAna0Xcth/tYAWQR6kFlWz7mwmge5qHuvdtD6SU6E1otPVcv7njwhykbNooJJTOZswWUUG9fQiyCMKgJzKbJYm7qTt3S9SXZyOpiwbTUUuTkGd2XYxh1G/8NYRBIHscg3NPUGetob8a7uQ+bTAUluEWpPOh0OCUStknMwoQbSKBCs0ZAsQ0rI/KldvTq+fg3tg8wafc0B4HG9sP0KUrxv3xAVjtljZe6UAk8XKy3fd6mRurS5hzuThJGcVsnxCuwbH8lQKaKtLbNo53I7VpGv0W+nMGS+w+ONmeATE4uYTVr/dqKvh/M8fEx0ZTGVlJR4eHg2e78CBAwcO7hwOQfMn+HDB20xrpqCp/61qn0gfV97sq2B+ggZnZVGDlUU36RzpS7iXM1cKq5gztDU6o5mRXx5g/s6LPNGnGW4qBTqjmZWn0tlzJZ+tT9hW0MilEuYOa8tTa0/TIcwbQYAPx3Wq3z+2fQQn00tYuCeZlwbXvfjXJ2bhEdyD6E5jEU+8Q7SfW4MuwhPjg/gpw4razReFkwvl14/g5SRFq9Wz5HgObk5y+jcLrBdXqUXVfHbwKq/d04a3d1zkm5FRZFdcx0utJMijLlfmWlEVYV4uN5accomPcuX44c8RRXBSSBGtFgSp/a+kaNYRFhxMgVlGcn4lconAg12b1PeRslgsPLH6JPnVeso0enTGxpd+ZvUL5Yl9W/EKetFuX3l+CsH+Ho0mnkkkEo4f/IkO3QehdPHG1TsMXXUJ2qoiWg96EpnCiWdemMXybz5v9PoOHDhw4ODO4BA0f4LrF84wsav9MoOXsxKZsQh31a93K/ZyVlKlN9W/QHMrtLg61XWknvjtEZwVMnQmM3KJhLHtIxocQxAESmp0nMos5btftEAA6BLly9G0YvIqtKxNyESjtxDUrDdWixlf58Y/fmelDLWTktLknbiWHOOptl4EN3fhXG4V3x23sCs5n92X83GSSzGarYR6OTPv3na4KOXcHRfMy5sSeKZfC4I81BjMFn5OyuPw9WLmjWiHk1xKtJcTs95/B09PT+IiAlAr5BSnHCSghe1yjtViwlyRjk/bbtzTux85Wz5nREu/+v0Wi4Vhnx9gfMdIxneIRCGT8NnBq6SX1BDl62p3X3HBnnhbznP58DJiOt+HXKmu8wDKuUTmqdUknNj7q5/Z18u+p93QGajcfDFoK1E4udq4IKecK0Kv1+PkZO++7MCBAwcO7hwOQfMnkAiNV7zra6ooqtI1uh/gSkElvWL8yS7TMGtzInkVtbw4sBWdIm95qoiiyNNrT2OyWBsc44N9l3m4R1OqdPaOtjcZ2iqYaSuP0T7Mi6f6NWPh2ZWo3QPRVWsaPSezVENZZTX9PU4zc9StaqRwbxfuaRXIM2tP8/qwNvi5quzODXBX0yHch0PXivj+dDpVOiPuKgXvj2pfX37tpxJY/O47bFj5He+MjKd9mDdPbTlIaZoa76gudTk9mnJSD3xGv45xLPj4C6RSKS8c3MuPF68yvIUvSpmUR1adZGLnKCZ2iqq//n3tI1i8N5l37o23i7boTRaahPkS378zXy17F6tEiWg2EhsVzPnTBwgICGj0mQBcu56Bc1AbBEFA7eZnt19w8qS0tJSQkJBfHceBAwcOHPy1/KOcgisrK3nttdfo1asX8fHxTJgwgbNnz/7X5qP2CaRCa7DbbrZYMRu0VBksJOVXNHiu1mgmo0zD+sQsQr2ckUslqBQyGzEDdRGYxWM6cjC1yG6MSq0Rk9lCiKfarkfR7TjJpIiiiEou41BqAe/1dePz3hY8ZQauFti6ENfoTTy79Tr/2qVB8InlTLkTb+zOsBFUMqmEGYNasfZMZoPX23w+mzHx4TzSM5ZXBrdCKZNwJrOUSd8dYdXJNKxWkYT0AgpO7ODu1iF0jPBBIhH4eEQMw2WH0RycS9XBt5GeXYhMk8V3369BJpMhCAKLPv+a1pNf4dmfs5j+/QlKNXru+0X0ytfViXviQnllUyLXius6kYuiyNmcCuYczOftT75mzuyXyb52ntyUU+SlnePAnu2/KWYAwkKD0FUXN7pfNFTh5dVwfo4DBw4cOLhz/KMiNM8//zxlZWUsXrwYLy8vVq9ezbRp09i4cSNNmjT57QH+Yp555Q1enjKKeYOjUd7ol2S1iizck8zkLk0oqjUzc0MC80fG0+a2SqSSGj0vb0pAbzRTpjUy/9542oV589rWcw1eRy6T0NTfla9OZvNw59D6qENKUTVNA9xJzqvk4LVCxsQ33ARz28UcPNUKvJyVPN7nVsfp+fe24+VNCYR6uXOlSooBJelFZbQY8hItb+u/lFWYwks/fc/iYdH12yJ9XEjMKUNnNNeXcZstVpadSKNFoAduKgVzt5/nVGYpY9qF88qQ1sgkEnYm5TJ6yUGcFTKuaA2smtqDdWcz2X4xB5PVSpiXC+8OalnfMPO+r2z7YAmCQPcevfjcSUWUygOT+VbF1e10j/bD01nBq1vOIZNKcJJLKdUYcPENJjom1u7438uzT05nzEMvo2473m6fobaS8EB31Gp1A2c6cODAgYM7yT9G0GRlZXHs2DF++OEH4uPjAZg9ezaHDx9m+/btPPPMM3d8DmlpaXy+8B0qC3OxiNCue19w82fOlnN4qBTIpBJKNXpGtQunQ4QPFqvImjMZfHfsGkU1BryclRhMFowWC1O7RRPm6czO5HzahXkDIFIXuVE3YCJXqTOx90wmp3KqCFCYqTWYMJqtmKxWfF2caBnkwZnMUjpG2EZ4Cqt1ZJfXEunjyr962L7IneRSQvz8OWJsTmivEWhzL+PvV27XTNItoCm5GeHklNcSeltJuYdKwbydlzBbrFQbLLgoJNwdF0Lv2AA+PXCF87kVfHl/V5tz7usQyYDmQUxedpQoH2f+teoEd8eF8OmEzjgr5SRml/HypgTahHjydL8WDSZVPzF5AjPi3Qj1CuT+rw83+FldK67mm2PXWTy2I0E3OplbrSK7LucRF+bHpezi+gaYf4Tw8HCG949n+9GdeMcMqPek0VTkob2+nRXrvv3DYzpw4MCBgz/PP0bQeHp68tVXX9Gq1a0yXUEQEEWRqqqGmzf+lezasZ0fPniT6e198Q9xAeBC+h7y0lP4ZHQbVHIZFqsVt9sSgaUSAaQSPhzXmYxSDbM3J7BwzK0X7DfHrjG0VXD98WPiw/n8UAovDGhhk/txpaASrdFETXUVHVr7oZA6sT4xk9lD4vjowFW+mtgVqwjv7U7iyPUihrYKQSmTsu1iDilFVcwbEc/ivcl25eP5lVpOVfsQ3mMMAOW5SUTG2zZjvIkyvDvHMzcx7oY4uZRbTusQL6Z2iyarTMPChGoei3cj0scFq1XkQEoBbUO8bcTMTVyUcoLd1eRUaPlsQhcbf5mOET50CPdm9pZzbLuYTanGwNQpkwkICOS1118nKekS0dIqQr3qRFeVzljftfx2vjyUwrx74218eCQSgSGt6nJb+nbpwLHEiw3e62/x6qwX6bBrN5988R1VOguIFjq0bc6chascy00OHDhw8F/iHyNo3Nzc6N27t822nTt3kp2dTY8ePRo979eaBRYUFBAYGNjo/pvo9Xq+XjiXBf1DbYRGmxAPPh7Vhrd/usSH93W0Oy+9pAZnnyC+O5nBsFaBvD+6I8/9eBqdyYJUENCbLAxsHnTbeF7kV+qYuTGB/s0CcVcp2H4xh+slNXiplfi7qdh2uQyLSYdSJuGjA1fpExtwo7s0vHJXHPmVWvZdLcBosXLsehFrH+kD1HnWpBRVcTi1CB9XJ0a0DmHDpVJcmz1Yf32pQoVRr0Ghsi/jtuircVHViYMavYn39yTjoVIS6plPWq3A7PmLWDj7OeZ0U5BdrsFFKWdQiyC7caxWkVe3nmNoq2CKNQY7szyoE6qP9IjlqbWnaB7gTjvdVcqSztO35beYZCo+G3Wr0eWbw9ry0sYEvn2wW72YvJRXQesQLztTwZsMbhHM18cONbivMUwmE4WFhahUKnx8fBgyeBBDBg/6Q2M4cODAgYM7xz9G0PyShIQEZs2aRf/+/enXr98dvdbGdWu4J1LVoD+Jr6sTVlGkpEZHZlktidllnMooQSaRYBFFKrRGCvHhw/1XuZBdSri3K0/3a06svxuHUgvZfjHHJq9lSKtg+jUL4ER6Ce/vSuKj+zoS6l0XEao1mHl89Qm0EikB7mrUcikuStuPMMhDzaQudflEZzJLsVpFimp0XCmoYtnx6/SI9qO4Rs+k744ilSlxCruV7xEU252si7tp3tO2v5MoipRe3olTJyVLDqdwvaSGeSPiCfFU8/XRayRWKXi3Wzc+XrmB155/grLsYpzkMjRG+z4wh68X0b2JH1nlmkZzfmr0Jhbvu8x3k7vj43Kr/Hly12gW703mwNVCxnWMBOr6VD3YJYqJ3x6hU4QPXSJ92Xc1n/EdoxocG+oiNb4uykb3347RaOSd117hWuIJQl0kaAxWygUVU594gYFDhv6uMRw4cODAwZ3nHylo9u7dy4wZM2jTpg2LFy/+1WN/rePsr0Vvbicj9Qq9vRtP9AzxUPPo9ye5t20YXZv44evqxKHUIp7o04wIbxe+PpqKzmQmxt+dRWM61Auj3rEB/JSUZ2frr5RJyavQcn/nqHoxA3XeMO+P7sC/Vh7njXvaMHXFMcpqjUzuGm03J5PFSlG1jv0pBXxxKIVPxne2Wf6Z0jWaN7Zf4OThJTS7ezYAand/FCpX0hO2EN76LqRyJUa9hswza3C2VmG2+tKvWSDTezWtH+fhnrHkHMhFq9USEhLCt+u2kJ6ezrS7e7IxMZsBzWyjNAdSCpk9pDUfH7iCSt7wr9+6hEym94y1ETM3eX5ASx77/gRj2kfUux8PbxPG8DZh/Hg2k08PXCGnopaBLYLtzr0dvem3+y6ZzWYeGjeSscFGHux9K5JnsYp8/vlcKivKGXv/A785jgMHDhw4uPP8o8q2AVatWsVTTz1Fr169WLp06X/EwCysSQxppbUN7rNYRc7llLN8ag8mdWlC62BPRrUL571R7fns4FWsosjjfZohUueP8ssoz5yhrfniUApzt18gIbuMQ6mFPLH6JNdLamwiGNeKq3llUyJfHE6leaAH7/ycxAOdositqGX7xRybMUVRZMHPl7i7VQjv7LzI+I6RdrksgiDw6tDWqDTXqa3Ir9/epMO9KNTuHF/7MgmbX8dwbS1TR3TmhZ4hDG4RTEwDrsIDQp3YtmVT/c9RUVE4+YYiUFfC/cu5KWQS4oI9OJ1Z2uAzvV5cTcsgjwb3QZ0QPHrdvox9eJtQXFUKpnZvztoLJY2eX1ito1RrbnT/TdavWU1PtxriAm3vWSoReLJzEBu+/QyjsXH/HwcOHDhw8J/jHxWhWb16NW+99RaTJk1i1qxZSCT/GT02etz93LPwLXo08bETJNsu5jCuQwQuSttqHKVcyn3tI9iZlMfIdmE81bcZnxy4SpcoX5vjXJ3kzLs3nsnLjpJTXouTXIrFamVoq2D2Xsnn6PViqvUmjGYL74/uUF8iLYoiy06k0SnCm/WJWWy9mEvfWH9qjRYOpBQQH+bN2A4RbE/KZfgvOm/fRCaV0NTfjbO738Xq3Ry1qxdUZSMzFPPkg8NRuPnyzcoNfH4liSX3Nr6E4+ssI6vItry6sqwUs0bP5vNZbL2Qw4DmgcglEi7lVZJRqqF/00BmbDhLv6YByH5Rdt1QGfbthHiq+Xj/VUQResX4IwhwNK2YtWczef2eNrx7pJgaz3g2X8rj3jjbii2DycKLG84y5/1PfvUaADs3/MCceJ8G9wmCQN9gOTu2bWXk6DG/OZYDBw4cOLiz/GMiNBkZGbzzzjsMHDiQ6dOnU1ZWRklJCSUlJdTU1NzRa6vVanwimvLWjouU194y0rtSUMnyk9fpFNHwS69NqCepN4zd/FxVlGnsTfhuIpdKuLdtGHe1DGZ0u3De352EySIye2hr3FUK5t0bXy9moO6FOrVbNDqTFVcnOQtHt0cqkeDjrOC+DhHoTBa+PJyKyWK1Ewy3o5BKcAlsTvOeD+If3Y2yiiKczFUc2bKWH5Z8hHd0T8K6PcTJ7MafcVKpgbj4DvU/l5aW4iU1sebh3tzXPhKL1cqqU+n8mJDJjIEtWHIkBalEYErXaF7deo7C6luOymaLlfwqXaPOyACJ2eW4KGWcySqta8654hhHrhWxcHQHYvzcMApO+LefwJrCEB7bdJ2DqUVczC1n6clc7v/hKv5xPbj//vsbHf8mUquxflmrIcLcFeRmpv3mOH8VGo2G0tJSrNbGn40DBw4c/H/lHxOh2bVrFyaTiT179rBnzx6bfSNHjmTBggV39PqvLljEwqce4ItDKRjMFsxWkUhvFyQIZJfX1pdi305uhRa/GwZxNXoTtcaGlzkySmvwc3Eit6IuQnMkrZiPxnUi0L1uTKPZ0mhfqHZhXhy9XsSxtGLu6xBRv31EmzC2Xcxh7+U8DqYW0r+ZfTWXKIpcKdIQNuoRJBIJcqWasOa9meJzkSgfV7xdlHywdxuXy+PYUaNjXDuLXeWQzmjmTJnAzJ696rclJyfTJsQdQRAYGhfC0LhftgEQmLriOM/0a8H0Xk354lAKeRVaBImEWquEWqsTm89nN9i/Sms0czytmNXTetW3UZiz5RwvD46r/9kJPVaLGf/2EzAbdXyVeQaxqBaFVxSK6GxeenmS3bgNYZEosFjFRkVNdpWRkIg7b+h44sRJ3njnAyq1IJE5IZgqGXZXH15+8dlGG2k6cODAwf83/jGC5tFHH+XRRx/9r10/Li6OVgNGkn9yJxPifPBQKTicUY5PtYKvjqXTOdJ+OWrVqXSeG9ACgJUn08gq09iZ3+VXannux7MopBLWJWRSUWvAZBHrxcxvYTRbSS+p4YtDKWw+n8PA5oFYRJF9VwvqOn8Pa8vCvZfpFOFjZ1K38lQGFt+29Ut3lRknEbMOkCYqKKs1cLmgiiB3FVmZp5B0fJ6pqz9jVv8mxIXUNeS8mFvOpydy+Xj1Zpt7Dw4OpvBX+lh1a+LLkrMFFMQOYd/Fc0T3iePtx54iPLwuZ8hkMtE2Kgi5VMKw1qH1giK/UsuszYnMGnJLvABIBGx+nhDnwZdJ2wloOxKZQoVvbJ3YMht11BYfpVOnTvwe7ho1ngN7v2VArH0EThRFDuSZWDFs+O8a69/lwKHDzHjtIwLaTcBPeuvz25lwjrTHnuHrLz++o9d34MCBg38K/xhB83fgxTlvkJw8lmVffExNbgW9Bz3EjiUT2LNzB8/NnckzvSKJ9HElt6KWb45dp1sTX7ydlRxIKWDzxXzWbN/DI+OG46eW4ePqRJXWSHaFlkndYhkfX5fncjK9hFKN3ua6cpmEap3RxrTvJhdzK3i6XwtKa/Xc1z6S8znlvLzxLCun9STUs65C6hWZlIdWHGN461C6RvlSVmtg1elMsix+RN71BAD5F3fgnreLCa196RXjX19hdDarjKuFVSQe+gJ3lZpXdmWh4gqeSgldIr3p39SPfT9to0WLW94wEomEs7k1WK2ijdC4yZHrxQwbN4knn3+xwecsl8s5n57P8IF9WfX1YXycFWgMZpoHuOHtrKR1iK15na+rE5llGiJuVIT1jfEhMT+ZE8dL8Ww5HKXag4q8S1hLEvj+249/d1Rj7ISJTNm4joCCalrdlhhssYp8cTqf0Q89+W+5Df8R3pz3AYHxkxAktpEx95B2XEreypUrV2jevPkdnYMDBw4c/BMQRFFsvGX0/zg3y7Z/rbT792AwGJgwqBtRCj3ncsrIrdTSPMCDcG8Xcitq8Xd1ohA3jIKMBb39UMmlGM1WDGYLi/Ze5s1hbevHSims4nh6CVO73SrFziit4cvDqcwd1ra+CaUoiqw6lY7OZOF8TjnRvq48P7BOVBxKKeTD/VdYOKZDvYNuYVUt01acwGixEuyhJt/kQtRdryGRKSg+9ilt1KUMbx2EyWxl95UCFDIJz/VvgVQi8O6uZI4IXYhqf2+9O3NZ6kGaag7z2sBIXjtUwLfbD9a/3FevXs1rC5bSRpHFuyPa2oia/Eotj6xJ5PilFDw8PACora0lMTERlUpFfHx8g8nepaWlPPPQ/VRlXeXjX5gYFlXr+PTgVeYOa2sjVrLLa1meUMyRHC2zXp3NA/eP+8MCxGAwMO/VV0g7f5JQFwm1Ritl/Gd8aK5fv84DTy3At0XD7s16TTkxymQ+/3jhHZ2Hg38Wf9X/NQcO/mk4IjR/AcePH6d3sBPDWkaQlF/BsevFjO8YicFswdvZCalEQGs089iaM6gVdb4sSrmUI9eL6BNrW4UT6+/GV0dSbXI3In1cmdotmte3n8dkFnF1kpFWUoNcKsFgNqMxmNEazYxbegipIPDeqHhqjWaeXJeAh1KCTCJQrjPz/FsLmThxIlMenIQuI52y89+hL87ks5HNCPO61Wm6Q4QPJ9NLeG/XJZ7p34JJnSNJPFFVLxYEQcCnaV+Sjl8jv1JLWy+BhIQE2rdvT4dmkTT3kuNs9aYo4gHuW/UDHQNkBLg5kZhbTbbFB6/Yjnh4eCCKIrNee4sDxy+CaySCxYBQO5eXn3+U4ffYigUfHx++37qbMXf1QW+yzeXxd1PRM9qfuTsu8kjPmPrlOo3BTI0gZ+vW9bRo2Yp/B6VSydz3FmMymSgoKKhLEPdpOAn8r6aiogJB4drofqWzB6VF5f+RuThw4MDB3x2HoPkLEEWxvlxs+8VcHu3V1C6JV62QEe6pokpnrN9nFUH2i+UPQRCY0CmKN7efZ+bgVvXl4BHeLugMZoyWOh+XUE9nrpdUE+LpzJLhbXFRyhFFkdOZpTy86gQPdY5C3WMsz740226+69ZvAODs2TNsm/8sYQ30W+oS5cu6hEzm/XQRqyiit9i7+jrHDGLr5e8JcFdgtVrp3q4Fz/eKoFdMAEtO5HJYV4H/oLc4lbAOSVoqMrknJouVUG81D48fyamkNJQRgwloOwmLyYBRV41U0YW5C5cRHBRI+/h2dtd8+uXXWf7BLKZ3sjXsG9QiCJlUyuuHCggPCsACtGzfg6/mPv2X9FeSy+WEhYX96XH+CNHR0Yi1BY3uryq6Rp/2bf9zE3LgwIGDvzEOQfNvoNFo0Gq1+Pr6IggCXbt2Zdm7IndT50D7y3YEN5FKpKw5k0FupRaJINAh3Jvj6cX0iQ2wOa5DuDfZ5Rq6vfsTwR5qlHIpWqMZs9nKticH4Hxj/JIaPW//dJFagxkXpRxBEOgc6ctr97Rh4e5kXhr/6y/gHRvWMjDK3ijvJvfEhSACnSJ8mP5jErqaElSut3x0BIkUsxXOlVkZExqKq6WWXjF19/JIl2Aq9x/hxOYfeHVIK+LDYhEEgUt5FXx64CgBQR5oDQr8g1uQf+gjfIUymrjLKdaYKdEpePb5mRw5uMduTr369CX5wmje3fkjD7TyIthTjdZoZvvVMq5avNhxeB9K5e9ra/B3x9PTk6ggN4qqiuw6oIuiFW3WIaZ/uea/NDsHDhw4+HvhyKHh96817/zpJ15+bArhHk64q+RcLaomuk0nfti8g88/WEj58c14SE3IpBK7xow55bXM2JjAiwNb0D7MG5NFZNflPFadSmdyt2iGtrxl1Z+UX8Gza0/TM8afJ/o0w8fFibwKLe/tTiKnXMPGx271riqq1rHsRBovDb61pCKKImO/OsSxlFxUKlWj9/PGSy8wULzcaEXVgZRCLFYrA5oHUarRM2VjLlpVEIIgJSC6C8aCBEb45qNqPwSjIMft0lbujgsho1TDktPFJGdmsXJKdzzUttGq68XVLN6XTJFrRxTV1/lkeIRNmwO9ycLTPyYw9eV5TJz04C+nBUBWVhZffbSQorxsFE5qxk+dTs/evf/nypirqqoYMWYSJve2eIa2RZBI0ZTnUX39Z96e/RRD7hr4356ig78ZjhwaB/9fcURoficnT55k7lNTWDW5a32EBOCH0+nc3acbOw4eZ0NgED8u/4rrKVdpFuBev5RjtYrM3naBrx/oWl86rZAJDGsdSpiXC99cNXCitIAgqrCK8M3RawxqEcTr97Stv06wp5oP7+vI1BXHuJhTQevQutJpf7e6ZazbEQQBtUL2q2IGYMjIMexaNIMH2jYsaI5cL+KZfnUVND4uTvgoDLj1m47VbCQneR+F1xORD5jEjNmvM2fOHHxlEvamlvJ5kgRJQD/ah22xEzMAO5PyeKxnM2b9fIGlE1rh5WwbUXGSS/lsXAfuf3UG9z8wqUGREh4ezrzFv+32+0/H3d2dPT9tYM269fy4cSNWEdrHRDJj/lcEBAT89gAOHDhw8P8Eh6D5nTw1ZTwfjelgI2YAJnSKYt/K49TU1DB6/P2MHn8/1dXVTJlwH2kpp/FxVaNy86BDkyA7HxiANiGeuKUX8e3W/bSKDmdahyC8XJQ8cVsH7psIgsDDPWKZsf4Mu58bVL9d8osXfoXWQLV9o2s7OnXuwkcGF/IrtXbGgInZZagVMptcILWTEkEQkMqVRLQdioubJ0FNmtctdXXuzNfblpNj9Sdk0Ktc37OQuFiPBq9botET4eNCpKtoJ2ZuIpdK6B/rR6uIAFq1jef9T5b8x3NY/i7I5XImTZzApIkT/ttTceDAgYO/Lf+Y1gf/bZQWPQFuDUc8hrUO4aOPPgJg37699GodjWdlGg/FBxLtCjkZaehqG25uCaDEjFwu5/6HprP2bCYyiUCge8PXCvZQI9xWBl2tM9r0PrJaReb9dIlHZsz5zXsSBIHPlq9habqUpWcLSCms4mJeBQt3J7Hrcj7P9rvlb2K1ipTU2nao9o7oxA8/bgMgICCAc7mVSCxaKg7Ox1qdS2aZpsHrejkrScqroE2w+6/OLz7Mm0c6hzPMW8N9fTvxw/erfvOeHDhw4MDB/08cEZrfya9lGlmtdeIgJSWFl6dNYPmkW0tLw9qEojOa+dfK41zKqyAu2NPu/Iz8EtqH+zCgeRDuKjnFNXrOZpfRJdLX7tjTGSWoFXUlywaThed+PIO7k5zD14oorNKx6Xw2rXsN5Mmnnvpd9+Xp6cnyDdu5dOkSW9evZdf673lnaDO7iM0P5wso1FgJFMVb5dsSKRZRIDMzkwUvPcUn4zrR+oaL8OrTRg6kFDZoCHh3q2CWHLlGQCOi7SYlGj2ezgq6NfGjXagXU16dwfB7R+LsbF+V5cCBAwcO/n/jiND8TswKNfmV2gb3bbuUy7PPPsuTDz3Am/e0tltaUilkvHNvPPN+umh37smMMjQ1lax7pA+P9W7Kt5N7IBNEPth7GYPJNiJSpTOy6lQ64Z4q5u64wBvbzzNzUCtyKmpZV6gmL6QbBy9ns3T56j98f3FxcYy5fxKlohuzf04nMacCi1Ukv1LLG7vT2VIciEeTbmQd+4b8Qx9TeGYV1SWZ+Hm7MefpR3i9h3+9mAG4r30Eoigyc0MCSXkV9duvFVfzxeFUAt1VJOVXYP6VJpRHrhXR+YaoUylkjIsP4Y3ZL//he3PgwIEDB//7OCI0v5MvVq3nsdGDWTKxS33EQRRFlh2/jmd4M5ydnakuzKFZYPcGzw/3dkEQBPZfK6VXlBc6k4VtV8tYcTiJlQ91t2mAuGpab0Z8vpfxXx9ibPsIYv3cSMwpZ9vFHCo0BloGe/DiwP9r787jY7wW/4F/ZjLZJovsIiQRV7OKLIQEIWJpa61WuVVaTaq0/fHtQrWl6pZr31pLVUujXEpRitJaailBElsrsWQnK5F9n5nz+yM3I2PCtWSb5PN+vfKSOeeZZ85zvCQf5znnOV4wNpAhLbcEpQrg14O/P9X1nTp1GlM+WYRSI0fod30Ri+JPQhGVBD1jS5i5TYLR3USYxv+MyT3bwrNNK6TnFWDtqdUw9QiAhbQIZkaaD4CT6UkxZ7gfJv0nEgdj07A1KgkSiQTtLOSYMagzrE0MEXH6Bj7dfR4LRnTR2iJha1QSujhba9xOC3SxxewTJ5/qOomIqHlioHlE/v7+WBSxA+PHj4a9XA9mRvpIzCmGT4+++HnLdgBVmyQ+jJ5MD2YDJ2D5wX0wMDTEP9+fjgPnX4Ctqeatl7aWJnjJ3wW5xaVYePAvGBvIUKZQYEqIOxJzijGu+z9gKNPDsWuZWHLoCn7YexgqlQpRUVEwNzd/or19ZvxrMaw8X0DWiY2IPf49DIxNYecSDDuXLijOy4BN+q9YOrqT+naTi40pFg73xuaYBNzOzwegvZt3m1ZyhLq1wXNebeFmrz1fZnyPZzBn/yW8vO44RnZxgpeDJTLySnDsehZ82llq7badV1oBuemDn5xLREQtFwPNY+jTpw8uJmWioqIC5eXlMDU11VhSLDO3RkpOEZz/u0liTVkFpVAamGD0mLEYPWasurxCeW9yTl5JBYrLFbBvZYypAzthyo9n8Zy/K4KdTVFYWok/E7JxI7sAkYm3YaAnhRACztZm+HLhXCReikJnBzMUlStxPacUU+cswcuj/1nrdeTn5+O773/A9RuJ8Pf1RmhIMPLLZMg+tgHPBI6CmbUjVMpKpMWdQNyJjbCQFmN+qGOty6fH+DkgfHPqA/usraUJtsck47PBPrXW38guwJ5TFzBuaH9IIMHBK2n4ZmwQDGV6WsdujUrGBwvXP/CziIio5WKgeQIGBga1bnK47JsIfDBmKL4dGwQD2b1bJQqlCp/9chHzVm3Sek+ZnjGOxqVjS2wxCvTbQGpkDpGbiOHPGOJuSQW6jhiBrzesxr+H+8LBQo41x6/hnT5u6Opsg1t5xfjqSBwKr5/HptcD1ecsVyjx7uwP4dDOET17at4C23/gN8yatxImTn1gbNEVVw4n4evv3kZaega6vvAZZAZVo0VSPX04duqHxPP7UJJwDnZmgaiNVCqBiZE+KhVK6NcSQi4VyBCdeKfWCdE/xSSjSBjgzp07eM7dDqM7t4ahTA//OZuIN3p01AhQey/fRJrCGCEhIbW2g4iIWjYGmjoUEBCAsI++wCtzP8Uw73bo1NYC1zPzsevSLbw2eZr6CZ41vf3Bx/hk7jJ0efELtDG6N7Kz7dx2ZOfHwvTKAfR1bf3fPZWATWHBkBtU/bW5tW6F1WMCMeuXC8gqKEXr/y4rN5TpYe4wH8x8720cjbo3ETk/Px+f/3sl2nR7Ux0WDNp5w9zeHTd3fg49mXZIc/Luj2vxv0PUWN10P7m5JeafTMP7QW3UE6LLFUp8G52JQWMmYH6/AXg20AdudmZ43qstyhRK7L6YipwKCWKup+Ly5cvq23Uj/Jxw8Eoapu6MwT9sTKEvk+JGVgES8hU4dzX58f9SiIioRWCgqWNvTHgLr4WF48vlS7H/fDTcu/fHmR3Toa+v/VA9AFg4bx469poIfSPN21TOAS8jK+EMVOlQFgAAHJxJREFUej1jh7YWJth1PgW3i8rUYaam8UEdsftiKib2dlOX2ZsbQ5Tkaxy3IWIzjJ17awUTPZk+2nmF4s7Nv2Dr7HtfnQFMjQxw6VYufB21N3ksLKuETXtX/L+PZmLlgjkozUmHBAISuSXCPpyP3iF9AQBx6bnYs2cPFi2YD2O5Kdb+chxublXt7dSpE5bnCrz833M+59UWz3o6IC2vBAqVQMfWVjDqO/6BfUhERMRAUw/09PTwwdSPHunYkgoBa0dvrXKJRAK5uR0OxWZgfI+OuJlbXOszbADAycoEmQVlGmVCCFSoNB+ecz0+EfJWvrWew9zGGQV3UrTK76acx0telthwOh6zBnfW2HOpQqHCwlPpmLVmE9zdPfDNf3566LUOHz4cw4cP1yhTqVT48+QJqOSWWHcqEW8GuUAqlVStiLI0QWFZJVZeyMeP/xzz0HM3RSqVCklJSdDX14ejY+1zkIiIqG4w0DSwjIwMvDByLFLS7wIAFCoJSgvvQG5up3WsgSjD3eKqoBLUwRaH4zIQ6q69mujizbtwba25a/afCdlw89Oc99LV3wd/HUiEgaOv1jlKchJQcTcJQtUXEmnVXJiS/CzcubAdo173wPMetlhxJA7mRvroaGuGG3eKcNfQDp8s+hru7o+/qgoAjh09jNXzZqGHnR5eai3DtWwZXv8hEsO8HdCtvQ2i0ktw7q4ES9dv0bkdtNd99z02bt0NlWFrCJUChqq7+PiDd7mZJBFRPWGgaUCZmZno1CUEfkOmIyDQGgBwNy0O1/7cDL9BH2gcW3QnCWaqfHRrX7VrdzcXWyw7HIvUu8XqTS+BqrkqSw5dwSfPVY3yqFQCR69l4puzN3HykuazaV59ZRTWbhgBZRtPjfkyFaWFMK5IhVN7G0Tvnw+lCpBJFOgZ6I8eLw/BqrOn8LKHBWYN9sGfCdnY+vddjHt3KsaHv/nEffH3338hYt4nmNfHUf0MHj9HK4z0bYeP98fhdrtuGDpxFKaFhOjcyMbab7/Hxl/Ow8Y3TF0mhAqfLVoPExNj9A7u1YitIyJqniRCPOyh/s1b9STdI0eONMjn/cPVG449JsLM2lGj/Ma5n1GafRXOXUdCX94KRcmRkGefRUFuJra/1Uf9C33z2UT8dOEWenawRs8OtkjKKcK+K5kYN2U6Du3dhdz0FCgF4NerLxZ/uQZyufYu2pf/+hsTJ08HWnlAz6Q1FAWp0CtORElJCSy9RsPEsmoEqDgvA0U39uCX7d+jvLwcG9euQmZGGrx8/DEu/C2Ym5trnftxTBo3CpOcS7W2RQCAu8Xl2JhliZXrtVeFNXUqlQq9+o+AtV+Ydp2yEoqEn/Db3m2N0DJqKRr65xpRU8ERmgaUV1wBT2tHKCrLUFpwG4ZyCxgYm+GZbiMQtfVD9CzYjtwsFbo6GGFHWi5G+FXNuxBC4GR8NnbH3saZq8k4ffo0Dh38FR26ueLM7omQSqWY/H/vPVIbOnt3wqmje3H06B+4kZAE387BWLt+E9L1usLI9N6kXxOLNtDzGIXpM7/Axu/WYNb8xXXaF6V30mHu3rrWOisTQxRkaM/n0QXx8fEQRva11kn19JFbVPnQFWNERPRkGGgaiFKphFAqkRH5HWwqU9HV1hBJeRVILjeHdbc3Ua4SOH49A0YyKa7mmsIjeDD2Hf8dxxKiUFqpgkNHTxw7HwsTExMMGDAAAwY8+VwMqVSK/v37oX//qtdTP1sMKx/tFUxGplaIv5T1xJ/zMP/r17mu/r6XyWQQQvngA1rugCgRUb1ioGkgEokErVR38b5nIYJcOqrL0/NK8NauOTBSFqC3pysySwWs2ntgztIVWrtKKxQK5Ofnw8zMDFJp3e0rKh4SL1T19PtXamaF0goFjGtZhl5YVgmDVtqTpHWBi4sL9Ctv1zoKo6gohYOdGUdniIjqAXfbbiAFBQVws5MjyMVao9zBQo4hHY0hygoQFZeI2KvXcTPqCMJHvwCVqmon6vz8fLw/MQxvDArGzFcHYexzwZg7czoUCkWdtM3WwhiKyjKtcmVlOewstefh1IW33puOb6Izcf8ULiEEvonOwqQPdXNXbYlEgo/en4SsS9uhUt77+1FUliH74mbM/Vw3r4uIqKnjCE0DiYuLQ/A/bGut6+/eGnsvp+DbcT1QrlAi4nQCjvx9EUcO/Y7gPiEIHzUMkzsZw7GPg/o952/F4N3xY/DN5u1P3bbZMz7Em/83G/b+YyHVq3p4nUqpQNbFrfh2xaynPn9tAnv0RMor72Dm96sxxMUE7a2MkHi3FPsTSzB60gfw79K1Xj63IQwd/DyMjY2xaNlq5BarIBEqtLExxca1i9DJy7Oxm0dE1Cwx0DQQGxsbJOUU11p3K68Epgb6EELAUKaHib1dkXC7AKuWLUZaajKGO0rgaKV5+8m/XStciknD+fPn4e/v/1Rt8/PzxepFn2LW3CXIKxEAJLCQC6xaOB3+/n5Pde6HGT32dQwZMRI/bdmMI0nxaB/kig1fvVrr6ixd0z80BP1DQ6BUKiGVSnmbiYionjHQ1KODBw9g/ifvQ19RhjKFCrn5BSgordBYqqxUCey5eBOt5JrLl18JcMEXh67hxG/7MMNXe8IuAAx2bYWdm79/6kADAIGB3fD7vu0oLS0FABgbGz/1OR+FiYkJxk+Y2CCf1Rj09LQ37CQiorrHQFNP1ny1AocjVmDFYG+0MjaAEAL7L6fhtYg/8XZvN/g5WSH5TjF+jE7CSH8nrDp2TeN/8WZG+jA1MwXw4CW++npSVFZU1Gm7GyrINDW5ubnYtGEdUhPi4dstEC+/Mlbnnk5MRNSScVJwPRBCYNOqxVg0wh+t/jsaI5FIMMSnHSaHeODnC6nYFp2M1NxizHjeG5vPJSK85zMa5zh4JQ0jx4XBp3swLqfl1fo5RxLyMfilf9b35TR7h387gLdfGginhN8xzjoLypOb8M/n+iAhIaGxm0ZERI+IgaYe7N27F6EdbSCVao+shLrbI6uoHFezi3D0ehbGbzyFXv+wQ3+Pe3s0xWXk4VhSHia/9wHemPgOfrhajPxSzZGYlJxiXCyRo3dISH1fTrNWUlKCbxbMwty+jujc1gJmRvoI7mCNfwXbYeaU5nsrjIioueEtp3qQm5uLVsb6tdZJJBJYmpvh10tJAIDY2FiEjxyCmJsxaG8lR/ydIuSqDLH7jzOQyWQwNzfHlxt/wmfvvQ2z8gw4mOghqVAJA/sOWLdlLSebPqXdO7ZjiItcK3yaGurDSb8E8fHx6Nix4wPeTURETQUDTT0YOnQoxi6dhZH+zlp1KTlFuJmVAx9Ha5QoVPi/T/+FM3FJuHz5Mq5cuYK3/f3h5uam8R5HR0dE7NyHzMxMZGZmwtnZGZaWlg11Oc1admYaPMy095MCADu5Hm7fvs1AQ0SkAxho6oGVlRXkbTviUFw6Bnjce3ZMWaUSn/1yActGdYV3Wyv8lZaLecvn4tSxI/hx1y/o3LnzQ89rb28Pe/va9wmiJxPUOxS/Lf0Vz9iZo6RCgZPxWSirUCKgvQ2u3FVggpdXYzeRiIgeAefQ1JPt+w/hWKEFJmw5h29OXse/9l3CqxtO4K1gV3i3rVqG7d3WEhte64Er5/5Efn5+I7dY96SkpKB/n15w7eCCV0ePfKI+7NY9ENcrzRERGY85+y/BQE8PDhZyREQmIDW3VGv7CSIiapoYaOqJVCrF1j0HsDfqGp796EucTsjCj2/2Qa+OmjtMGxvI0OcZO7wZ9kYjtVQ3DRv0PPwDByBT6g4rv9cRlSJBe1dfLPj33Mc6j0QiwaSpnyI1rxzzR3RBXzd7BLS3wafPeyPcxwoLv6ifJyUTEVHd0rlAo1Kp8NVXXyE4OBg+Pj4ICwtDSkpKYzfrgeRyOUJDQ2FlYgR9vdq729naFClJXCL8qJYtWYQzV9LRfdQ8uPgPhW17P7j1HIuAF2dj4VcbHnu59dbvvsZ7Ic9olXd3tsRfp/9Q76lFRERNl84FmjVr1uDHH3/E3LlzsW3bNkgkEkyYMAEVdfyAubqWX1qBO0XaG0ACwOmEbAx/cWQDt0h3LVi8Eh59wqEn01xJZii3gJPPIIx84YXHOl9ZQa7G05trsjIAioqKnrSpRETUQHQq0FRUVGDDhg2YPHky+vTpA3d3dyxfvhxZWVk4dOhQYzfvoXx6hmLegb+0dpe+kp6L2PR8fDpjZiO1TPeo9Ixhbqu9ggwA7Fy6IO124WOdT19uipKK2ncuz6sE59EQEekAnQo0V69eRXFxMQIDA9Vl5ubm8PT0RFRUVCO27H/btvNn3CzTw5j1J/HzxVScis/GrF8u4MMdMfhm2x4+T+YxqJQKKBWVtdZVlBVCKnm8W0SvTZqCTReztMr/ziiES+cA7sdERKQDdGrZdmZmJgCgTZs2GuV2dnbIyMio9T39+vV74PkyMjK0zlWfYq4m4cyZM5j0xmsoKb6Fvs8NQdzptfyF+Zja2hgjPe44HL37a9Uln/8F48e+/Fjn69ErGBfOvYR//7YDQzrIYWqoj2OpRcgycsCaiMV11WwiIqpHOhVoqneCNjDQnO9gaGioM8ueAwMDcTHuemM3Q6ft+/VX+HbtBQN5K9h16AqJRAKVUoHki/tRevs6vvj3r499znc/mIbb48Zjx9bNKCkqxOiwF/7nc4GIiKjp0KlAY2RkBKBqLk319wBQXl7+wF2ijxw58sDzPWz0hpouZ2dnnPnzMPqFDkRi1E+QGZqgsrQA9haGSIyP1Qq8j8rW1hZvT3m/jltLREQNQacCTfXtoezsbDg5OanLs7Oz4e7u3ljNokbg5uaGW2kpKCwsRG5uLuzs7DRCLhERtSw6NSnY3d0dpqamOHv2rLqsoKAAsbGx6Nq1ayO2jBqLmZkZnJycGGaIiFo4nRqhMTAwwNixY7FkyRJYWVmhbdu2WLx4Mezt7TFgwIDGbh4RERE1Ep0KNAAwZcoUKBQKzJw5E2VlZQgICMD69eufeN4EERER6T6dCzR6enqYNm0apk2b1thNISIioiZCp+bQEBEREdWGgYaIiIh0HgMNERER6TwGGiIiItJ5DDRERESk8xhoiIiISOcx0BAREZHOY6AhIiIincdAQ0RERDqPgYaIiIh0HgMNERER6TwGGiIiItJ5DDRERESk8xhoiIiISOcx0BAREZHOkzV2A4jo8UWdO4uINV+ivKgAVvZtMemD6ejQoUNjN4uIqNEw0BA1cbdu3cK2HzagqLAAIQMH41LMWdw8sRtv+djBzMgQmQU38cXE0Rjz/mwMHDS4sZtLRNQoGGiImrB5n8/AraijGNbRFGZGMuxffgI3MnKw8AVf9TH25saY2dsRny6dg9CBz0Im4z9rImp5OIeGqIn65eedQNxxTO3pANfW5mjTSg5UlmFCD+1bS1KpBD1by3Ds2B+N0FIiosbHQEPURO3Y+C1GdrLVKCsqr4SF3KDW4y0MgPy83IZoGhFRk8NAQ9REyRRlkOlp/hMNcLbByRvZtR5/7rYKQT16NUTTiIiaHAYaoiaqUiKDEEKjLKiDLU7GZyEtt0Sj/HxaAQwd3eHg4NCQTSQiajIYaIiaqH7DRuJIfI5GmVQqwYf9PTF1/1XM+zMD38VkYfaJDFw27YRFq9bhx61bEeLngV5eLhjcJwgXL15snMYTETUwLocgaqJeC38LUyJPIfNCAkZ42kJuoIfIlLvYlVCOn347DktLS+Tk5MDe3h5GRkZ49YVBMM9NxIrBz8Dc2ADJOUX46NUhGD15BsInvd3Yl0NEVK84QkPUREmlUqxc/wP6v78I69LMseCKBBVdR+E/+4/CyckJZmZmaN++PYyMjHDixAlIs65j2kAvmBtXTRpub22KVaMD8O2SL6BUKhv5aoiI6hdHaIiaMIlEgh49e6FHz4dP9l3w2Uf4pOczWuUyPSkGe9kjIiIC4eHh9dVMIqJGxxEaomagqCAfNqaGtdbZmxshLS2tgVtERNSwGGiImoGA4FCcjM+qte7ItWy89NJLDdwiIqKGxUBD1AzMWbAY35xOxt3ico3y86k5yFbJ4eXl1UgtIyJqGJxDQ9QMyOVyROz5HRNGDUXHVjK0tzbBhVt5qDSxwf6T5xq7eURE9Y6BhqiZ8Pb2xpm4ZMTGxuLGjRuYEhQEOzu7xm4WEVGDYKAhamY8PT3h6enZ2M0gImpQnENDREREOo+BhoiIiHQeAw0RERHpPAYaIiIi0nkMNERERKTzGGiIiIhI5zHQEBERkc5r0c+hyc7OhlKpRL9+/Rq7KUREdSIjIwN6enqN3QyiBteiR2gMDQ0hkzVspsvIyEBGRkaDfqauYl89GvbTo2sJfSWTyWBoWPvO60TNmUQIIRq7ES1J9WjQkSNHGrklTR/76tGwnx4d+4qo+WrRIzRERETUPDDQEBERkc5joCEiIiKdx0BDREREOo+BhoiIiHQeAw0RERHpPC7bJiIiIp3HERoiIiLSeQw0REREpPMYaIiIiEjnMdAQERGRzmOgISIiIp3HQFNH8vLyMGvWLPTu3Rv+/v545ZVXEB0dra6Pi4vD2LFj4evri5CQEKxfv17j/SqVCl999RWCg4Ph4+ODsLAwpKSkNPRlNKikpCT4+flh165d6jL2k6bdu3dj0KBB8Pb2xuDBg3HgwAF1HfvqnsrKSixfvhwhISHw8/PDmDFjcP78eXU9+4qoBRBUJ9544w0xbNgwERUVJRISEsScOXNE586dRXx8vLh7967o3r27mDFjhoiPjxc7duwQ3t7eYseOHer3r1y5UgQFBYljx46JuLg4ERYWJgYMGCDKy8sb8arqT0VFhXjxxReFq6ur2LlzpxBCsJ/us3v3buHh4SEiIiJEcnKyWLVqlXB3dxfnz59nX93nyy+/FD179hQnT54UycnJYsaMGcLf319kZmayr4haCAaaOpCcnCxcXV1FTEyMukylUokBAwaIFStWiLVr14rg4GBRWVmprl+6dKl49tlnhRBClJeXCz8/P7FlyxZ1fX5+vujcubPYt29fw11IA1q6dKkYN26cRqBhP92jUqlE3759xYIFCzTKw8LCxNq1a9lX9xk2bJiYP3+++nVhYaFwdXUVBw8eZF8RtRC85VQHLC0tsW7dOnTq1EldJpFIIIRAfn4+oqOjERAQAJlMpq4PDAxEUlIScnJycPXqVRQXFyMwMFBdb25uDk9PT0RFRTXotTSEqKgobNu2DQsXLtQoZz/dk5iYiLS0NAwdOlSjfP369Zg4cSL76j4WFhb4448/cOvWLSiVSmzbtg0GBgbw8PBgXxG1EAw0dcDc3Bx9+vSBgYGBuuzAgQNITU1Fr169kJmZCXt7e4332NnZAQDS09ORmZkJAGjTpo3WMRkZGfXc+oZVUFCAjz76CDNnztS6XvbTPcnJyQCAkpIShIeHIygoCC+//DKOHj0KgH11vxkzZkAmk6Ffv37w9vbG8uXLsWLFCjg5ObGviFoIBpp6EBMTg08//RT9+vVDaGgoysrKNMIOABgaGgIAysvLUVpaCgC1HlNeXt4wjW4gs2fPhq+vr9bIAwD2Uw1FRUUAgOnTp2PIkCHYsGEDevbsiXfeeQeRkZHsq/skJCTA3Nwcq1evxrZt2/Diiy9i+vTpuHr1KvuKqIWQ/e9D6HEcPnwYU6dOhY+PD5YtWwYAMDIyQkVFhcZx1T8o5XI5jIyMAAAVFRXq76uPMTY2bqCW17/du3cjOjoae/furbWe/XSPvr4+ACA8PBwjRowAAHh4eCA2Nhbff/89+6qGtLQ0TJs2DREREejatSsAwNvbG/Hx8Vi5ciX7iqiF4AhNHdq8eTMmT56M3r1749tvv1X/cLS3t0d2drbGsdWvW7durR7qru2Y+4fKddnOnTuRk5OjXlrr5+cHAPj8888xePBg9lMN1dfj6uqqUd6xY0fcunWLfVXD5cuXUVlZCW9vb41yHx8fJCcns6+IWggGmjqyZcsWzJkzB6+++ipWrFihMXwdEBCAmJgYKJVKdVlkZCRcXFxgbW0Nd3d3mJqa4uzZs+r6goICxMbGqv/H2RwsWbIEv/76K3bv3q3+AoApU6Zg3bp17KcaPD09YWJigkuXLmmUX79+HU5OTuyrGqoDybVr1zTKr1+/DmdnZ/YVUUvR2MusmoPExETh5eUl3n33XZGdna3xVVBQIO7cuSMCAgLE9OnTxY0bN8TOnTuFt7e32LVrl/ocy5YtE926dROHDx9WPwdj4MCBzf45GDWXbbOfNK1evVr4+fmJvXv3ipSUFLFmzRrh7u4uzpw5w76qQalUijFjxojnnntOREZGiqSkJLF8+XLh4eEhLly4wL4iaiEYaOrA119/LVxdXWv9mj59uhBCiEuXLolRo0aJTp06ib59+4pNmzZpnEOhUIhFixaJwMBA4evrKyZMmCBu3rzZGJfToGoGGiHYT/fbsGGDCA0NFV5eXmLYsGHi0KFD6jr21T15eXli9uzZIiQkRPj5+YnRo0eLs2fPquvZV0TNn0QIIRp7lIiIiIjoaXAODREREek8BhoiIiLSeQw0REREpPMYaIiIiEjnMdAQERGRzmOgISIiIp3HQENEREQ6j4GGiIiIdB4DDdEj+vjjj+Hm5qb+Cg8P1zpm1apVGseEhoZi7ty56tfHjh3TOP748eMax99fv2fPHnXd+vXr6/HqiIh0GwMN0ROKjo5GRUWFRtmZM2e0juvSpYv6+wsXLmjUnT59WuN1zQ0SAWhsTsmNEomIHoyBhugJlZWVISYmRv26tLQUFy9e1DquZhC5f/fsRw00crkcXl5eT9tkIqJmi4GG6Am0bdsWABAZGakui46ORmVlJQCgXbt26nJbW1s4OzsDAP766y+oVCoAQE5ODm7cuAEAMDU1BQBcvXoVhYWFAIDy8nJcu3YNAODr6wuZTFafl0REpNMYaIieQPfu3QEAp06dUpdVh5t27drBwcFB4/jq205FRUWIj48HUDU6U703bFhYGABAqVQiOjoaAPD333+rA1LN21ZERKSNgYboCXTr1g0AEBsbi/z8fAD35s9U19VU222n6ttNFhYWeP3116Gvrw/g3m2ny5cvq98TEBBQ15dARNSsMNAQPYHqERqVSoUzZ84gLy8PcXFxGnU11TYxuDoAde/eHaampvD29gYAnDt3DgDU83H09fXh4+NTPxdCRNRMMNAQPQEHBwf1PJlTp07h7Nmz6rkxtQWa9u3bw9bWFkDVCE1ycjLS09MBAD169ND4My4uDoWFheoRGm9vbxgZGdXvBRER6TgGGqInVH1rKTIyUj1/xtHREW3atKn1eH9/fwBAQkICfv/9d3V5dZAJCgoCUDXqc+DAAXXg4XJtIqL/jYGG6AlVj8Skpqbi4MGDAGqfP1OtOpgIIbBx40YAVaulnJycAAA+Pj6Qy+UAgO+++07rfURE9GAMNERPqOatpdzcXK2y+9UMJnfu3AFwb1QGqJorUz35NyUlBQAglUq5womI6BEw0BA9oTZt2sDR0VGj7GGBxt3dXf28mWrVt5se9Lq29xARkTYGGqKnUPMWk5OTE+zt7R94rFQqhZ+fn/q1RCJBYGCgxjH3v+btJiKiR8NAQ/QUao7IPGz+TLWaAcXV1RXW1tYa9W5ubrCxsVG/5vNniIgejURUP6qUiIiISEdxhIaIiIh0HgMNERER6TwGGiIiItJ5DDRERESk8xhoiIiISOcx0BAREZHOY6AhIiIincdAQ0RERDqPgYaIiIh0HgMNERER6TwGGiIiItJ5DDRERESk8/4/hbWqLOF2Z/8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,4))\n",
    "sns.scatterplot(x=\"MW\", y=\"LogP\", data=df_pIC50, hue = 'bioactivity_class',size='pIC50', edgecolor='black')\n",
    "plt.xlabel(\"MW\",fontsize=14, fontweight='bold')\n",
    "plt.ylabel(\"LogP\",fontsize=14, fontweight='bold')\n",
    "plt.legend(bbox_to_anchor=(1.05,1), loc=2, borderaxespad=0)\n",
    "plt.savefig(\"Plots/MW_against_logP_plot.png\",bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the compounds have a molecular weight of between 200 and 500 Da and logP values between 1 and 6. This is reasonable as drugs with MW>500 and logP>5 tend to lead to poor absorption.[⁶](https://www.sciencedirect.com/science/article/pii/S0169409X96004231) Good absorption is needed for orally administered drugs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mrov3ce4LIqKSU7pPmXNdEBFpnlKh/P5cF2UhICAAV69exZ49e8RtFy9exKZNm/C///0P1apVQ7du3fDVV1/BxMSk0PO4u7sjJiZGZlufPn2wevVqjdVORKQqpUK5qLkuNGHXrl3w9/eHm5ubuC0sLAxTp07FtGnT0K1bNzx79gzfffcdkpOTC/2lkZaWhtjYWGzdulVc7BVAkSFORKRNah99IZFIkJCQgAMHDpT4uQkJCfj888+xfv16NGjQQGbfwYMH0a5dO0ycOBH16tVDp06d8PXXX+PEiRPIzs5WeL7IyEhIpVK4urrCxsZG/DI3N1fpvRERaZpK45Szs7OxePFiXLx4ESkpKZBIJHLHCIKA4cOHl+i89+7dg6WlJU6cOIFNmzbJdDuMGzcOBgbyv0Nyc3ORlpYGa2truX0RERGwsbGBhYVFieogItIWlUL5xx9/RGBgoLprgbu7O9zd3RXua9Kkiczj7Oxs7Ny5E02bNlUYyEB+S9nU1BQ+Pj4IDw+HtbU1BgwYgFGjRikM+AJFrcQdFxcHe3t7Jd4NEVHJqRTKZ8+ehSAIsLe3h52dHcLDw+Hh4YGcnBxcunQJUqkU/v7+6q5VlJubi1mzZuHRo0fYt29focc9fPgQqamp6NmzJ6ZOnYqwsDCsXr0aKSkp+OqrrzRWHxGRqlQK5djYWADA6tWrYWlpiV69eqF///749NNPsWXLFqxbtw4XL16Ep6enWosF8i/eTZs2DdevX4e/vz+cnZ0LPXbnzp3IysqCmZkZAMDJyQlv377F5s2b4ePjU2hrOSQkpNBzFtWKJiIqLZUu9OXm5gIAatasCUdHR1hYWODOnTsAgO7duwMAQkND1VTiP168eIHPPvsM4eHh2LZtW6FdHQUqV64sBnKBhg0bIj09HSkpKWqvj4iotFQK5Ro1agAA/vjjDwBA48aNERISgtzcXNy8eRMA8OrVK/VU+P9SUlIwevRoJCUlYf/+/WjXrl2Rx0skEri7u2Pz5s0y2+/cuYMaNWqgWrVqaq2PiEgdVOq++Oijj3D48GEsXLgQrVu3Rps2bbBhwwZ8/PHHSEtLgyAIsLW1VWuhK1asQFRUFLZv3w5ra2skJiaK+6ytrWFoaIjU1FTk5OTA2toaBgYG6NatG7Zv34769eujadOmuHr1KrZv3w5fX1+11kZEpC4qhfKUKVPwxx9/ICEhAQ4ODujfvz9+/PFHmS6BgQMHqq1IiUSC06dPIycnB6NHj5bbHxISAgcHByxbtgyhoaHi1KIzZsyAhYUFfvjhB8THx8PBwQG+vr4YMmSI2mojIlInQaripBZpaWm4cOEC+vfvDwC4evUqAgIC8PbtW3Tt2hWTJk0q8SRG+qDgQl9RFwOJiN6nbHao1FIGADMzMzGQAaB9+/Zo3769qqcjIiKoeKHP09MTP/74o9ov5hERVXQqhXJUVBTWrl2Lzp0746uvvhJHYRARUemoFMp169aFVCpFbm4uzp07h/Hjx7P1TESkBiqF8rlz57B//34MGTIEZmZmkEqlcq3nK1euqLtWItIjoaGhGDdunEZuJCvPVB59USA7Oxvnzp3DyZMnceXKFfFuPwMDA/z9999qKVKXcPQFUfEyMzMxefJkvHr1CtWrV8eWLVsq/DzmymZHqedTNjIyQvv27dGhQwc0bNhQHAbH5aOIKq7AwEAkJSUBAJKSkjQyq2R5pfKQuJycHFy8eBHHjh3D5cuXkZeXByA/jK2trdV68wgR6Y/Y2FgEBgaKDTOpVIrAwEC4u7ujVq1aWq5O96kUyosWLcLp06fx5s0bAP+0it3c3DBs2DB07doVlStXVl+VRKQXpFIptmzZIveXcsH2RYsWlcubytRJpVB+d6knCwsL9OvXD8OGDYOjo6PaCiMi/RMdHY3w8HC57RKJBOHh4YiOjkadOnW0UJn+ULn7okWLFhg2bBh69eoFY2NjddZERHrKwcEBLi4uuHXrlswycQYGBmjZsiUcHBy0WJ1+UCmUg4KC0KhRI3XXQkR6ThAETJ48GVOmTFG4nV0XxVNp9AUDmYgKU6tWLQwaNEhm26BBg7i2pZJKPSSOiOh9Xbt2LfIxFY6hTERqt2rVqiIfU+EYykSkVjdv3sSDBw9ktj148EBcKo6KxlAmIrWRSCRYuXKlwn0rV66UGZFBiqkcyhkZGXj58iVevnyJjIwMddZERHoqLCwMb9++Vbjv7du3CAsLK+OK9I/SQ+KkUimCgoIQHByMe/fuITk5WWa/lZUVmjVrht69e6Nv374c+kJUARW3YLK6F1Quj5QK5bS0NIwfPx63b98GoHiyodevX+Py5cu4fPkyDh06hB9//BFmZmbqrZaIdFrdunVRt25dPH/+XG5fvXr1ULduXS1UpV+UCuV169bh1q1bAIDq1aujcePGsLKygpGREQAgKysLycnJiIiIwMuXLxEeHo7169fD19dXc5UTkU4qyIX3cT4c5SgVyufOnYMgCPjiiy8wdepUGBgo7oqWSqXYuHEjNm3ahLNnzzKUiSqYqKgoPHr0SOG+R48eISoqiq3lYih1oe/169cAgD59+hQayED+rZS9e/cGALk+ZyIiKp5SoVwwiciqVavw+PHjQo97/PgxVq9eDQD8bUhUARU3XzLnUy6eUt0XQ4YMgZ+fH3799Vf8+uuvMDIygpWVlTg7XHZ2NlJSUpCZmQkgv8U8fPjwUhUWEBCAq1evYs+ePeK2+/fvY9myZbh79y6srKzg7e2N8ePHF3meM2fOYMOGDYiKikL9+vUxc+ZMdOrUqVS1EZFiZ8+eLXZ/r169yqga/aRUS3ns2LEYO3YsBEGAVCpFVlYWEhIS8Pz5czx//hzx8fHIyMgQR2V4e3vjs88+U7moXbt2wd/fX2bb69evMXbsWNSvXx9HjhyBj48P1q9fjyNHjhR6nmvXrmHmzJkYMWIEgoKC0KFDB3z55ZdFtvaJSHVNmzYt1X4qwTjl2bNnY/jw4Thz5gzu3r2LmJgYcZC4qakp7Ozs0LRpU3Tt2hUNGzZUqZiEhAT4+vrixo0baNCggcy+n3/+GUZGRli4cCEqVaoER0dHPHv2DNu2bSt06alt27bB09MTI0eOFN9DeHg4du/ejcWLF6tUI5E+K2hUaYqtrS3q1KmDqKgouX1169aFra2t+Be1uhkbG5eL+yNKNJ9y3bp1MWnSJE3Vgnv37sHS0hInTpzApk2bEBMTI+4LCwuDm5sbKlX6p+R27dph69at4oq575JIJPjrr78wZ84cme1t27bF+fPni6yjYNVZReLi4jgFIeklqVSK2bNn4/79+1p5/efPn2PIkCEaO3/jxo3h5+en98Gs8sojBUJDQ3HlyhVkZmaiQYMG6NmzJywsLFQ6l7u7O9zd3RXui4+Pl2uB16xZE0D+Qo3vh/KbN2+Qnp4OOzs7uefExcWpVB8RkaYpFcqNGjWCgYEBfv31V/E2ydzcXMyYMQPnzp2TOXbt2rVYsGABevbsqdZCMzMz5QalF1xoVPTnWMGfSIqeU9yfbyEhIYXuK6oVTaTLBEGAn5+fRrsvCqSkpODzzz8HkH/TyM6dOzW+bFyF6754/9bqDRs2KLzSmpKSgpkzZ8LW1hatWrUqfYX/z8TEBNnZ2TLbCn64TE1N5Y5/d2TI+8+pUqWK2uoi0ieCIMDExKRMX3P69OmwtLQs09fUZyrNEieVSnHw4EEIgoAmTZrgP//5D4KDg7Fu3To4ODggLy8PW7duVWuhdnZ2ePHihcy2gseKJjmxsrKCqampwue836VBRJrTunVrbZegV1QK5devXyMlJQUAsGbNGnz00UdwdHRE9+7dxVEN6p7Q2s3NDTdu3EBeXp647erVq2jQoIFcfzKQ3yJwdXVFaGiozPbr16+rtQVPRKROJQrlgv4aS0tL8U+gatWqyRxjbW0NADLhqQ4DBw5EWloafH198ejRIxw9ehS7d++WGQ2SmpqKpKQk8fHYsWMRHByMnTt34vHjx/j+++9x//59jB49Wq21ERGpS4lCuX///hg6dChmzZoljrA4c+aMuP/Nmzfibdb16tVTY5n5s9Nt374dT548gZeXFzZu3IhZs2bBy8tLPGbZsmUyq+h26NABy5cvx4EDB+Dl5YVr165hy5YtcHR0VGttRETqIkgVTY78nkaNGsk+6Z0rnPb29rh48SIA4NNPPxXHFs+dOxejRo1SZ606oWD0RVEjNIgquszMTAwePBgAcPjw4TK/uKiLlM0OpUZfnD9/HhEREXjw4AEiIyMRERGBqKgoSCQScawwANSoUQPR0dHo3Llzqee+ICKqiJQK5Tp16qBOnTr49NNPxW0ZGRl4+PChzJjHbt26YdiwYejXr1+5GC9IRFTWVL6jr0qVKmjRooXMtrFjx5a6ICKiiqzEQ+JiY2MVbk9KSkJiYmKpCyIiqsiUDuXXr19jwoQJ6NGjh9xdcgBw4MABdOnSBcuWLVO4n4iIiqdUKGdlZWHUqFG4dOkSsrOzFd4Y8uuvvyI3Nxd79+7FlClT1F0nEVGFoFQoHzhwAA8fPgSQvzSUonX6WrZsCUtLS0ilUly5cgVBQUFqLZSIqCJQKpR/+eUXAPnBe/z4cYX3ss+bNw8nTpzABx98AAA4evSoGsskIqoYlArlR48eQRAE/Pvf/1Y4I1sBW1tbTJ8+HVKpFA8ePFBbkUREFYXSfcpA/njl4jg5OQEA0tPTS1EWEVHFpFQo29jYAIBSC47+73//AyA/URERERVPqVBu3bo1pFIp1q1bJy6Wqsjbt2+xdu1aCILA6TGJiFSg1B19I0eOxIkTJxAREYG+ffvC29sbLVu2hLW1NSQSCV69eoWbN29i//79iImJgSAI4grSRESkPKVCuUWLFpg0aRK2bt2K2NhY+Pn5KTyuYMK5MWPGcLUBIiIVKD33xddff41q1aph/fr1yMjIUHiMsbExpkyZIjPxPBERKa9EExKNGTMGAwYMwNmzZ3Hz5k28fPkSeXl5sLa2hrOzM7p3765waSYiIlJOiWeJs7CwwODBg8UJrImISH2UCuWEhASVTq5olWkiIiqcUqHcuXPnEk9aLwgC/v77b5WKIv2wd+9eHD58GIMHD+ZoGyI1UXrqTqlUWuIvKr9SUlJw6NAhSCQSHDp0CCkpKdouiahcUKql/O6K0UQA8N1338k8XrBgAdatW6edYojKEaVCecWKFZqug/TIzZs3xdvpCzx+/Bg3b95Ey5YttVMUUTmh8hp9BVJSUsS7+OrUqQMzMzN11KXQ9evXMWrUKIX7HBwcFC7dfezYMcyZM0du+7lz51CvXj2111jeSSQSLFmyROG+JUuW4PDhwwrn2yYi5agcyhEREVi6dCnCwsLEbYIgoH379vj222/h6OiolgLf5eLigsuXL8tsi4yMxMSJEzF58uRC62zTpg3WrFkjs93a2lrt9VUE169fL3S5r+zsbFy/fh3t27cv46qIyg+VQvnhw4cYMWIE0tPTZS7oSaVS/PHHHxgxYgQOHDggTnivLkZGRuKMdQCQk5ODFStWoGvXroWOm46MjESjRo1knleeSaVScapVTYiIiCh2v4uLi0Ze29jYuMSjgIj0jUqhvHr1anG2uBYtWsDFxQVSqRR37txBeHg43rx5g9WrVyMgIECtxb5v3759iIuLw3/+859Cj4mIiEC3bt00WoeukEqlmD17Nu7fv6+1Go4cOYIjR45o5NyNGzeGn58fg5nKNZVCOTQ0FIIgYMyYMZg9e7bMvrVr12Lr1q24fv26WgosTFZWFrZs2YLRo0ejZs2aCo9JSkrCy5cv8eeff2LPnj1ITk6Gs7MzvvnmGzRo0KDQc3t4eBS6Ly4uDvb29qWun4hIEZVC2cjICJmZmRg0aJDcvr59+2Lr1q0wNDQsdXFFOX78OLKysuDt7V3oMZGRkQAAQ0ND+Pn5IT09HQEBARgxYgROnjyJGjVqaLTGsiYIAvz8/DTafVFg5MiRMq9jYmKCPXv2aPQ12X1BFYFKody5c2ecPHkST548kbugV7A2n6enZ+mrK0JQUBC6du1a5Aon7dq1Q2hoKCwtLcVtmzZtQpcuXXD06FFMnDhR4fMUjeIoUFQrWhcIggATExONv46fnx+mTZsmPl6zZk2ZvG55o+lrANqSmZmp8PvyRFONBJVCedasWQgPD8fChQuRlZUFFxcXZGdn488//8QPP/yAWrVqYfTo0XJzZqhrLoykpCSEh4crNUXou4EMAKampnBwcFB5Pg/KV7t2bfF7JycnpdZvJHlZWVnlfnKvov6a1WeHDx/WSENEpVDu06cPsrOz8fbtW3zzzTdy+1NSUtCvXz+ZbeqcC+Ovv/6CIAho06ZNkcft378f69evx2+//SZ+eGlpaXj69KnCrhdSzdKlS7VdAlG5oVIov379WvxeG3NcPHjwAHXq1EGVKlVktufl5SEpKQnm5uYwMTFBly5dsG7dOsyaNQs+Pj7IzMzEmjVrYG1tzVvHSed809YGRoblp8+8IBvK03WA7DwpVl9P1OhrqBTK2g60ly9fwsrKSm57XFwcPDw8sGLFCgwYMAD29vbYvXs3Vq9ejeHDh0MqleLjjz/GTz/9xP5P0jlGhkK5CmWgPL2XsqNSKGt7LoyFCxcq3O7g4CB3c0Pjxo2xY8eOMqiKiKj0OEkBEZEOYSgTEekQhjIRkQ5hKBMR6RCGMhFpxFMTAdvtK+GpCUdhlARDmYjUTgrgNysDvDIS8JuVAbhip/IYykSkdk9MBMQb58dLvLEBnrC1rLRSLwdFROqRnVc+2pNSAL9bGkKQSiEVBAhSKX63NEDtt7l6fztJWfwbMZSJtOjdaQo0fftuWTGwN4bxB/+s9CMVBCSYCPB7kgJJXPmZEU9TU0yw+4KI1KqysyWkEtnAkkqkqOxsWcgz6F1sKRNp0buT9ZSHCYmeVhFwrEZlue2CgQChhhFGeNiifob+dtO8OyGRpiZaYigT6Qh9n5BICuCqtSEglQKKAksqxVVrQ/wrPk/v+5Y1id0XRKQWeQDeVBIUBzIACAJSDQXklWlV+octZSJSi0oARsflIr2I5TlN8xg6xeHnQ0RqY5GX/0WqYyhrGBfG1G9cQZvKGkNZw7gwpn7T1OKYRIXhhT4iIh3ClnIZqvqv/hAMys9HXh4XxgQAqSQXbx8GabsMqqDKT0LoAcGgUrkK5fIVxdpXXua+KFAef2lz7guiCqS8zH1BpcM+ZVKZxDwZuU1uQGKerO1SiMoNtpRJJVJIIan9FKiSAUntpxAeOENgh0aJGRsb4/Dhw9ouQ+0yMzPFUTl79uwplyNYjI2NNXJehjKpRGqRDFRNy39QNQ1Si2QIb6pptSZ9JAhCuQysd5mYmJT796hOehfKMTExcHd3l9u+dOlSheOBX79+jaVLl+L3338HAHTv3h1z586Fqampxmt9n1SSW+avqQlSSCGxf5o/A40AQIr8x8lm5aK1XF7+nUg/6V0oR0REwNjYGBcuXJC5qmtubq7w+H//+9/IysrCrl278ObNG/j6+mLRokXw8/Mrk3rfnQi7vAyzMrA3hrHZP5OYQwBg9hYZb06Vq0nMAc1NZE5UGL0L5cjISDRo0AA1a9Ys9tjw8HCEhobi9OnTcHR0BAAsXrwYn3/+OaZPnw5bW1tNl1suFUxiLhj880uxYBLzrLgXWqyMSP/pXShHRETgww8/VOrYsLAw2NjYiIEMAG3atIEgCLhx4wZ69uyp8HkeHh6FnjMuLg729vZK1/tua7483DwitUiGpMYDue0Fk5hXdfsUwhursi9Mjd69eaQ8jbEl/aB3CREZGQkbGxuMGDECT58+Rb169TBlyhR07NhR7tiEhAS5ADUyMoKVlRXi4uLKqmSRvt88IoUUEofof/qS5Q+AxCEahg+ql4u+ZSJt0KuEyM7OxtOnT1GlShXMmjULpqamOHHiBCZMmICdO3eiffv2MsdnZGTAyMhI7jzGxsZFztwWEhJS6L6iWtHlniAFjLIKv5VPwP/vlwJShjKRKvQqlI2MjPDnn3+iUqVKYtg2a9YMjx8/xo4dO+RC2cTEBNnZ2XLnycrK0sroC30nSA1geL8lUCmn8INyK0OQ8p4kIlXp3f8eU1NTudZvw4YNkZCQIHesnZ0dXryQvfCUnZ2N5ORkXuRTkZBjDCHDrPCvHM0MqCeqKPQqlB88eAAXFxeEhYXJbL97967Ci39ubm6Ij4/Hs2fPxG3Xr18HALi6umq2WAWkktxy9SXJy4EkL0frdWjii0hb9Kr7omHDhvjXv/6FRYsWYcGCBahWrRp+/vln3Lx5E4GBgcjLy0NSUhLMzc1hYmICZ2dnuLq64uuvv8bChQuRnp6OBQsWoH///lppKZeXccpEpDl61VI2MDDAli1b0Lx5c0ybNg1eXl64desWdu7cCScnJ8TFxaFDhw44ffo0gPzhTBs3boSDgwNGjx6NadOmoVOnTli4cKF23wgRUSH0qqUMANbW1li+fLnCfQ4ODoiIiJDZVr16dfj7+5dFaQpxwhn9pqlJZ4gKo3ehrG844QwRlYRedV8QEZV3DGUiIh3CUCYi0iEMZSIiHcJQJiLSIQxlIiIdwlAmItIhDGUiIh3CUCYi0iEMZSIiHcJQJiLSIQxlIiIdwlAmItIhnCWOqAKRSqVFLhqsLpmZmQq/1yRjY2MIgv4v2MtQJqogpFIpZs+ejfv375fp6xbMu61pjRs3hp+fn94HM7sviIh0CFvKRBWEIAjw8/Mrk+4LIL9lXvC6ZYHdF0SkdyrCSjj6jt0XRKQRoaGhGDduHEJDQ7Vdil5hKFOphYWFabsE0jGZmZkICAhAYmIiAgICymwERnnAUCaVvNsvuX79ev6nIxmBgYF49eoVAODVq1cIDAzUckX6Q6/6lJOTk7FmzRr897//RVpaGpycnDBjxgy0bt1a4fHHjh3DnDlz5LafO3cO9erV03S5WlFW41D37t0rfp+ZmYldu3ZhzJgxGn3N8nIhp7yLjY3F4cOHZbYdPnwY7u7uqFWrlpaq0h96FcrTp0/Hq1evsGbNGlhbW2P//v0YP348jh49CkdHR7njIyIi0KZNG6xZs0Zmu7W1dVmVXKa0NQ4VAIKDgxEcHKzR1ygv41DLM6lUii1btkAikchsl0gk2LJlCxYtWsR/v2LoTffFs2fPcOXKFSxYsACtW7fGBx98AF9fX9ja2uLUqVMKnxMZGYlGjRrBxsZG5svQ0LCMqyeqGKKjoxEeHq5wX3h4OKKjo8u4Iv2jNy3latWq4ccff0SzZs3EbYIgQCqVIiUlReFzIiIi0K1bt7IqUevKYhzq8+fPMWPGjEL3//DDD6hbt65GXpvdF7qvdu3aMDc3R2pqqtw+c3Nz1K5dWwtV6Re9CWULCwt07txZZtuZM2fw/PlzdOjQQe74pKQkvHz5En/++Sf27NmD5ORkODs745tvvkGDBg2KfC0PD49C98XFxcHe3l61N1EGND0O9fXr18Xub9iwocZen3RbTEyMwkAGgNTUVMTExKBOnTplXJV+0Zvui/fduHED3377LTw8PODu7i63PzIyEgBgaGgIPz8/rF27Funp6RgxYgRevnxZ1uWWGzVr1izVfirfimuw6HKDRlfoTUv5XRcuXMA333wDZ2dnuYt4Bdq1a4fQ0FBYWlqK2zZt2oQuXbrg6NGjmDhxYqHnDwkJKXRfUa3oiuDFixfF7i/uLxEqv06ePFnsfi8vrzKqRj/pXUt579698PHxQadOnbBt27Yi/1R/N5ABwNTUFA4ODkhISNB0meWWm5sbqlatqnCfmZkZ3Nzcyrgi0iWXLl0q1X7Ss1Dev38/lixZgs8++wzr1q2DkZFRkce2bdtW5qaGtLQ0PH36FB9++GFZlFsuGRgYKBz7DQBz5syBgYFe/UiRmjVv3rxU+0mPQvnJkydYvnw5PD09MWnSJLx69QqJiYlITExEamoq8vLykJiYKIZwly5dIJVKMWvWLDx8+BB37tyBj48PrK2t+edTKbVs2RKNGjWS2daoUSM4OztrqSLSFYruFyjJftKjUD579ixycnJw/vx5dOjQQeZr2bJliIuLQ4cOHXD69GkA+RcUdu/ejbdv32L48OEYM2YMzM3N8dNPP3GWLDWYN2+e+L0gCDKPqeK6fft2qfaTHl3omzx5MiZPnlzkMRERETKPGzdujB07dmiyrArL0tISQ4cOxeHDhzF48GC5/nuqmFxdXXH27Nki91PR9CaUSfeMHDkSI0eO1HYZpEPatWsHQ0ND5OXlye0zNDREu3bttFCVftGb7gsi0g+FXYAv6sI8/YOhTERqExYWhoyMDIX7MjIyOPe2EhjKRKQ2rVu3hrm5ucJ9FhYWhU6zS/9gKBOR2hgYGGDWrFkK982aNYvj2JXAT4iI1Kply5Zo0qSJzLYmTZpwHLuSGMpEpHbffvutOM2qgYEBvv32Wy1XpD8YykSkdpaWlhgyZAgMDAw4jr2EOE6ZiDSC49hVw1AmIo0YMWIEUlNTYW5ujv3792u7HL3B7gsiUrtbt26JK5Ckpqbi1q1bWq5IfzCUiUjt3p+gihNWKY+hTERq5efnV6LtJIuhTERqk5WVhcuXLyvcd/nyZY2utF5eMJSJSG1WrFhRqv3EUCYiNZo7d26p9hNDmYjUyNjYGB06dFC4r2PHjjA2Ni7jivQPQ5mI1Gr27NkKtxc2URHJYigTkdotXbq0yMdUOIYyEamds7OzOK+yubk5Z4grAd5mTUQawVurVcOWMhGRDmEoExHpEIYyEZEO0btQlkgk8Pf3R8eOHeHs7Ixx48bh2bNnhR7/+vVrzJgxA25ubnBzc8P8+fORnp5ehhUTESlP70I5ICAABw8exNKlS3Ho0CEIgoAJEyYgOztb4fH//ve/ERUVhV27dsHf3x9XrlzBokWLyrhqIiLl6FUoZ2dn4z//+Q98fHzQuXNnNGrUCGvXrkVCQgLOnz8vd3x4eDhCQ0OxYsUKNG3aFO3bt8fixYtx/PhxJCQkaOEdEBEVTa+GxD148ABv375Fu3btxG0WFhZo0qQJ/vzzT/Tq1Uvm+LCwMNjY2MDR0VHc1qZNGwiCgBs3bqBnz54KX8fDw6PQGqKjo2FoaFjkMURE74uLi4OhoWGxx+lVKMfHxwMA7O3tZbbXrFkTcXFxcscnJCTIHWtkZAQrKyuFxytDEARUqqRXH5vGFHyG73/GRAB/Pt5XqVIlGBkZFX9cGdSiNhkZGQAg98aMjY2RkpKi8HhFH4KxsXGR87qGhISUstKKoeCvBX5epAh/PlSjV33KJiYmACB3US8rKwtVqlRReLyiC4BZWVkwNTXVTJFERKWgV6Fc8GfQixcvZLa/ePECdnZ2csfb2dnJHZudnY3k5GTY2tpqrlAiIhXpVSg3atQIZmZmuH79urjtzZs3+Pvvv9G6dWu5493c3BAfHy8zjrngua6urpovmIiohPSqT9nIyAgjR47E6tWrYW1tjdq1a2PVqlWws7ODp6cn8vLykJSUBHNzc5iYmMDZ2Rmurq74+uuvsXDhQqSnp2PBggXo378/W8pEpJP0qqUM5N8MMmjQIMybNw/Dhw+HoaEhduzYASMjI8TFxaFDhw44ffo0gPyREhs3boSDgwNGjx6NadOmoVOnTli4cKF23wQRUSEEqVQq1XYRpJ94dZ2Kwp8P1TCUiYh0iN51XxARlWcMZSIiHcJQJiLSIQxlIiIdwlAmItIhDGUqsdevX+Pw4cPiY29vb8yZM0eLFZEqnJyccPTo0TJ7vdjYWAQHB4uP3d3dsWHDhjJ7fX3BIXFUYnPnzkV0dDT27NkDAEhOToahoSHMzc21XBmVRGJionj3a1nw9vZG7dq1sXLlSgBAUlISjI2NUbVq1TJ5fX2hV7dZk254//e4lZWVdgqhUrGxsdHq61tbW2v19XUVuy8qqIcPH2LKlClo27YtmjVrBk9PT+zevVvcf+XKFQwbNgzOzs7o1KkTfvjhB+Tl5WHOnDk4duwYQkND4eTkBOCf7ou3b9/CxcUF+/fvl3mtzZs345NPPoFEIoFUKsW2bdvg4eEBZ2dn9OvXDydOnCjT90753u2+mDNnDmbOnAk/Pz+0b98ezs7OmDJlChITE8Xjb9y4gbFjx6JVq1Zo1qwZevfujVOnTsmc89SpU+jXrx9atGgBDw8P7Ny5E0D+z0hoaCiOHTsGd3d3AP90X0RFRaFRo0b47bffZM41b948jBgxAkD+7I6rVq1Cx44d4eLigiFDhuDy5csa+2y0iaFcAWVkZGDs2LEwNTXF/v37ERwcjB49emD58uW4f/8+bt26hc8//xwtW7bE0aNHsXz5chw+fBj+/v7w9fVFjx494OLiIvefomrVqujWrRtOnjwps/3kyZPo168fDAwMsHbtWuzfvx/z5s3DyZMnMWrUKCxcuBD79u0ry4+AFDhz5gySk5Oxd+9ebNy4ETdu3MDatWsB5K/iM27cODRq1AhHjx7F8ePH0bx5c8ydOxcvX74EAPzyyy+YOXMmevXqhRMnTmDGjBlYt24dDh8+jA0bNsDFxQU9evRAYGCgzOvWqVMHbm5uMj832dnZOHv2LLy8vADkd5ldunQJq1atwrFjx9CjRw9MnjwZ//3vf8vmwylD7L6ogDIyMjBq1CiMGDECZmZmAICpU6di69atiIiIwKVLl9CiRQvx4p2joyOWLFmCFy9eiH2QlStXVvjn74ABAzBq1ChER0fDwcEBd+/exePHjxEQEID09HTs2rUL33//Pbp06QIAqFu3LmJiYrBjxw589tlnZfchkBwzMzMsXrwYlStXhqOjI/r16ye2XrOzszF16lSMHz8eBgb5bblJkybh6NGjePr0KWrUqIFdu3ahR48emDhxIgCgfv36ePv2LapUqQIrKytUrlwZJiYmCrstBgwYgMWLFyM9PR2mpqb49ddfkZ2djR49euDZs2c4deoUAgMD0bx5cwDA2LFj8eDBA+zYsQOffPJJ2XxAZYShXAFZW1tjxIgROH36NB48eIBnz57h/v37AACJRIKIiAh89NFHMs/x9PRU6txubm5wcHDAqVOnMHnyZBw/fhwuLi6oX78+bt++jaysLMyePRtz584Vn5Obm4vs7GxkZmaW2UUnklevXj1UrlxZfGxubo6cnBwA+a3ZgQMHYu/evXj06BGePn0q/szk5eUBACIiItCjRw+Zcw4ePFip1+7WrRsWL16MkJAQ9OnTB8ePH8enn34KMzMzXLp0CQAwatQomefk5OTAwsJCtTerwxjKFdDLly8xZMgQVKtWDR4eHmjfvj2aN2+Ozp07A8hf4FEQBJXOLQgC+vfvj5MnT2LChAk4ffo0vvrqKwD/XCBct24dPvjgA7nnKrOoJGlOUZ//48ePMXz4cDRp0gQff/wxPDw8UK1aNZnQLc3PjampKbp3746TJ0+iY8eO+P3337F161YA//zc7Nu3T26kRkGrvTwpf++IinXy5EkkJyfj4MGDmDJlCjw9PcWFZ6VSKRwdHXHnzh2Z5+zatUvs3yvuP56XlxceP36M/fv3IzU1VWw9ffDBB6hUqRJiY2NRr1498eu3337Djh07yuV/sPLiwIEDqF69Onbt2oUJEyagc+fOYl9yQWgq+rlZvnw5pkyZotRrDBgwAH/88QeOHj2K6tWro3379gCAf/3rXwDyl3179+fm6NGjOHLkiLreos7g/4IKyM7ODhkZGThz5gxiY2Nx+fJlTJ8+HUB+3+Hnn3+OmzdvYt26dXjy5Al+++03bN26VZwf19TUFC9evEBUVJTC89euXRtt27bF2rVr4enpKY5fNjc3x7Bhw7Bu3ToEBQUhKioKx44dw6pVq1CjRo2yefOkEjs7O8THx+O3335DTEwMzp07Jy4WUbA48cSJE3H69Gn89NNPeP78OYKDg3Hw4EGx66tq1aqIiYlBfHy8wtdwc3ODvb09Nm7cKF4YBvJDuUuXLliwYAFCQkIQFRWFHTt2YOvWrahTp47m33wZY/dFBdS9e3fcu3cPfn5+SEtLQ+3atTF48GCEhITg9u3bGD58OAICAuDv74/t27fDxsYG3t7emDx5MgCgf//+OH/+PHr37o3z588rfI0BAwbg2rVr6N+/v8z2uXPnwtraGv7+/uKCt1OnThUvDpFuGjVqFP73v/9h1qxZyM7ORv369TF9+nT4+/vj9u3b6NSpE9zd3bFkyRJs27YN33//PWrXro1vv/1W/Atr2LBhmD17Nvr27YurV68qfB0vLy+sX79e7udm7dq1WLt2LRYsWICUlBTUqVMHS5YswcCBAzX91ssc7+gjItIh7L4gItIhDGUiIh3CUCYi0iEMZSIiHcJQJiLSIQxlIiIdwlAmItIhDGUiIh3CUKZizZkzB05OTnJfTZs2RevWrTFgwADs27dPbkWSd59X2K212hYTE4O3b9+Kj6Ojo8WafX19VTqnt7c3nJyc0KRJE7l92dnZePLkicr1llZRtZFuYCiTynJzc5Gamop79+5h8eLF4tpr+iAlJQV+fn7o3r07Xr9+XSavefr0afTs2VNutQ6id3HuCyqRwMBA2NjYQCqVIjc3F0+ePMG8efOQkJCAn376Cd7e3nBwcACQP8/FtGnTAGh/Pbj3rVq1SmZF7gL29vbixO5VqlRR6dzr169Hdna2zGx6N27cwNdff61asVShMJSpRGxsbGBnZyc+rlOnDry9vbF69WpIJBL8/fffYihbWlrC0tJSW6UWqbApXwwNDWXenyoUrazBKWZIWey+oFJ7dx7kdwOpqD7lu3fvYsaMGejYsSOaNWuGzp07w9fXV+F0oLGxsZg/fz7c3d3RokULuLq6onfv3ti4cSOysrJkjs3KysLmzZvRp08ftGjRAm3btsWwYcNw+vRp8Rhvb2+ZdeI8PDzERWAV9Sl/8cUXYj9sQkKCzOsdP35cPL5gEdL3+22PHj0qs9TVxo0bxeMHDBgAJycnODs7y/RtA/lzERec+8GDB4V9/ACAN2/eYNWqVejWrRuaN2+Ojz76CGPHjlV6cdGSfMZBQUEYNmwYWrVqhSZNmqBt27YYP348bty4IXOcRCIR5+F2cXFB06ZN8fHHH8PHxwcPHz5Uqq6KiC1lUll2djb+/vtv7N27F0D+JPaurq7FPu/48ePw9fUVlxoCgPj4eAQGBuKXX37B1q1b0bp1awBAamoqvL29ER0dLR6blZWFhw8f4uHDh4iNjcXy5cvFesaMGYO//vpL5tjw8HCEh4fj2bNn+OKLL0r8PocNG4aLFy8iLy8PJ06cwIQJE8R9QUFBAPLnin5/KSRlDBw4EPfu3UNmZiYuXryIPn36AMhvWRdMi+rk5IRGjRoVeo7Xr19j2LBhePr0qbjt1atX+OOPP3D16lX4+fmhX79+hT6/JJ/x7t27xe8LJCcn4/Lly/jzzz+xe/duuLi4AABWrlwps0I6kL/qzblz5xAaGoqff/4Z9erVU+JTqljYUqYS6dy5s9h6a968OYYOHYrY2Fh8+OGH2Lx5c7Grh8TExOC7775DTk4OrK2tsXr1apw+fRoLFiyAqakp0tLS4OPjg7S0NADAhQsXEBcXBwBYuHAhLly4gH379qFWrVoAgIsXL4rn3rlzpxjIPXr0wPHjx3Ho0CE0btwYALBp0ybEx8dj/fr16N27t/i8Q4cOyS1v/66OHTuKXTIFIQzk/yK5du0aAKBfv36F9kH36NEDGzduFB+PGTMGv/32G3r06IE+ffrA2NgYABAcHCwec+vWLcTGxgLIn5u6KGvXrhUD2dvbG6dOncKuXbtQq1YtSKVSLF++HBkZGYU+vySfccFfGI0aNcKhQ4dw4cIF+Pv7o2rVqjAwMJD5i6Sgz/6jjz7CsWPHcOHCBSxduhSGhobIzc3FhQsXinxfFRVbyqQWUVFROHDgAGbOnIlKlQr/sTp8+DAyMzMBAIsWLULXrl0B5C8llJmZCT8/PyQlJSE4OBhDhw6Fl5cXevbsiejoaDg6OgLIbxHb2dkhNjZWXMYK+CfULC0tsXLlSnER1mXLliE0NBROTk4wMzODmZmZzAKtNWrUKLIf2cDAAEOGDMGaNWvw6NEj3LlzB82bN8fx48chkUgAAEOGDCn0+VWqVEG1atXEx2ZmZuLrValSBZ6enjh16hQuX76MN2/ewMLCAmfPngWQv+7du79A3ieRSHDmzBnxM/T19YUgCPjXv/6F5cuX4/Hjx2jYsCEMDQ0LPUdJPmMrKysAQGJiIsLDw/HRRx/B09MTbdq0gZWVlczFzWrVqiE9PR3R0dG4desW2rdvj8GDB8PDw0NhvzvlYyhTiRSMvgDy/+MmJiZi9+7dOHv2LHbt2gVjY2NxaSlF3u0bfX/F7HcfR0REiN8nJibizJkzCA0Nxf379/HmzRtxX0EoAsCzZ88AAA0aNJAJ3aZNm6Jp06YlfasyBg0ahA0bNiAnJwfHjh1D8+bNcezYMQCAi4uL2CetioEDB+LUqVPIycnBhQsXMGDAADGUO3bsWORSWa9fvxY/j0aNGsmEYvv27cV17oqj7Gc8c+ZMTJw4Ea9evRKHQJqbm6N169bo1asXevXqJf61NH/+fEyfPh3Pnz8Xl46ytrZG27Zt0b9/f3zyySdK1VbRsPuCSqRg9IWdnR3q1q2LVq1aYfXq1TA1NQUA8WJXYYpqsb2rIFyuXr2Knj17YsOGDXj58iWGDh2KjRs34tNPP5V7TsEIh3f7qtWlevXq4muePn0a4eHh4k0gRbWSldG+fXvUrl0bQH5r//bt24iJiQEAcSmlwrw7qiM3N1el1y/JZ9yiRQtcuHABy5YtQ7du3VCjRg2kpqbi119/xTfffCMz7K9Lly4ICQnB/Pnz8cknn8DS0hJJSUk4c+YMJk2ahO+//16less7hjKVmoGBgRii748geF/Bn8cA5NZp++OPP8TvCy5sbdq0CVlZWbC0tERQUBC++eYbeHp6KgzegotGT58+FfukAeDmzZsYOnQo5s6di9u3bwOQXZFb2eFqw4YNA5DfOl2xYgUA5S/wFfV6giCI4Xvt2jUcOHAAQH5XQZcuXYo8r7W1tbgw7b1792RatWfPnsVnn32G+fPni39FKKLsZ5ybm4uIiAhcunQJdevWhb+/P65cuYILFy6ILfJffvkFCQkJyMrKwr1793DlyhW4urpi69atuH79Ok6dOoWGDRsCAPbt24e8vLwi319FxFCmEklMTER8fDzi4+MRFxeH+/fvY968eWIYFzf6ol+/fmKf88KFCxEcHIzHjx/j4MGD2LBhA4D8VmlB0BWcNzU1FcHBwXjy5Am2bNmCS5cuiecsaCH27NlTfM6sWbPw999/4/bt21i6dClu3ryJoKAgsS+z4OIaAHF0RnHatWuHDz74AED+hbiC96PMTSbvvl5kZCQeP34sXlwD8lvEgiAgNzdX/GujZ8+eMDIyKvK8BgYG6N69O4D84XwLFy5EZGQkQkNDsWrVKoSFhSE4OLjIm3eU/YyzsrIwcuRITJs2DV999RUuXLiAqKgoJCQkiN0dgiDA0NAQL1++xODBg8XW85UrVxAdHY0XL16Ir/fuL3P6B/uUqUQGDRpU6L4qVapgxowZRT7f0dER8+bNw5IlS/Dy5Uu5/mczMzP4+/vDzMwMANC1a1f8/fffkEgkmDNnjsJzvnz5EnZ2dhg3bhz++9//4ubNmwgJCUFISIjMcV9++aU4iuLdIWYzZ84EIN9yV2To0KFiKxlQvuuifv36qFKlCjIyMnD+/HmcP38eM2fOxOeffw4AcHBwQNu2bcXRHEDxXRcFpk+fjtDQUDx79gyHDh3CoUOHZPbPnz9f7F5SpCSf8fz58zF79mwkJSXhyy+/lDtu+PDhYh/4V199hTVr1uDp06cYN26c3LFffvllsaN1KiJ+IqQyQRBQuXJl2NjYoFu3bjh48KBSE90MHz4chw4dQu/evVGzZk1UrlwZtra2GDhwIIKCgsQxygAwceJETJ8+HXXr1oWxsTFq166N/v37w9/fXzymIHyNjY2xe/du+Pj4wNHREUZGRqhWrRpatWqFtWvXYurUqeJz+vTpgwEDBsDGxgbGxsZo2LChOCqkKF5eXuJFxJYtWyp9gc/MzAwLFy7Ehx9+CCMjI9SsWRMWFhYyxwwcOFD8/oMPPkCLFi2UOre1tTV+/vlnjB07FnXq1BH/TTp06ICdO3cWG+4l+Yz79u2Lffv2oWvXrqhVqxYqV66MqlWromXLlli8eDHmz58vPmfSpEn48ccf0bFjR9ja2qJSpUqwsLBA27ZtsW7dOvEXEskSpLz/k0gnnDp1SvxL4+uvv8bkyZO1XBFpA7sviLQoLS1NHMv7ww8/AMgfm1zUHXhUvjGUibTo9u3bGDt2rMy24cOHw97eXksVkbYxlIm0qE6dOqhRowbevHkDW1tb9O3bV+EFNKo42KdMRKRDOPqCiEiHMJSJiHQIQ5mISIcwlImIdAhDmYhIhzCUiYh0CEOZiEiHMJSJiHTI/wEJifCWP+grYgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 350x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(3.5,4))\n",
    "sns.boxplot(x=\"bioactivity_class\", y=\"pIC50\", data=df_pIC50,showmeans=True)\n",
    "plt.xlabel(\"Bioactivity class\",fontsize=14, fontweight=\"bold\")\n",
    "plt.ylabel(\"pIC50 value\",fontsize=14, fontweight=\"bold\")\n",
    "plt.savefig(\"Plots/pIC50_plot.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, the pIC50 values for active compounds are lower than for inactive compounds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('The difference between active and inactive compounds is statistically significant. ',\n",
       " 'p value is 5.059831920548439e-127')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitney(\"pIC50\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 350x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(3.5,4))\n",
    "sns.boxplot(x=\"bioactivity_class\",y=\"MW\", data=df_pIC50,showmeans=True)\n",
    "\n",
    "plt.xlabel(\"Bioactivity class\",fontsize=14, fontweight=\"bold\")\n",
    "plt.ylabel(\"MW\",fontsize=14, fontweight=\"bold\")\n",
    "plt.savefig(\"Plots/class_against_MW_plot.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('The difference between active and inactive compounds is statistically significant. ',\n",
       " 'p value is 4.9351373174942564e-86')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitney(\"MW\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 350x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(3.5,4))\n",
    "sns.boxplot(x=\"bioactivity_class\",y=\"LogP\", data=df_pIC50,showmeans=True)\n",
    "\n",
    "plt.xlabel(\"Bioactivity class\",fontsize=14, fontweight=\"bold\")\n",
    "plt.ylabel(\"LogP\",fontsize=14, fontweight=\"bold\")\n",
    "plt.savefig(\"Plots/class_against_logP_plot.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('The difference between active and inactive compounds is statistically significant. ',\n",
       " 'p value is 2.486260332683729e-59')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitney(\"LogP\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 350x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(3.5,4))\n",
    "sns.boxplot(x=\"bioactivity_class\",y=\"NumHDonors\", data=df_pIC50,showmeans=True)\n",
    "plt.xlabel(\"Bioactivity class\",fontsize=14, fontweight=\"bold\")\n",
    "plt.ylabel(\"Number of hydrogen bond donors\",fontsize=14, fontweight=\"bold\")\n",
    "plt.savefig(\"Plots/class_against_NumHDonors_plot.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('The difference between active and inactive compounds is statistically insignificant.',\n",
       " 'p value is 0.2026376755980096')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitney(\"NumHDonors\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 350x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(3.5,4))\n",
    "sns.boxplot(x=\"bioactivity_class\",y=\"NumHAcceptors\", data=df_pIC50,showmeans=True)\n",
    "plt.xlabel(\"Bioactivity class\",fontsize=14, fontweight=\"bold\")\n",
    "plt.ylabel(\"Number of hydrogen bond acceptors\",fontsize=14, fontweight=\"bold\")\n",
    "plt.savefig(\"Plots/class_against_NumHAcceptors_plot.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('The difference between active and inactive compounds is statistically significant. ',\n",
       " 'p value is 1.2959599018241656e-14')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitney(\"NumHAcceptors\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Interpretation of results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Active compounds have higher MWs, higher logP values and higher numbers of hydrogen bond acceptors. The difference in number of hydrogen bond donors for active and inactive compounds is not significant. There may be some hydrogen bonding happening between active compounds and the target binding site. However,this cannot be confirmed without further studies. High MW and high logP tend to result in poor absorption as mentioned above and these metrics can result in undersirable non-specific binding interactions. However, majority of the active compounds have MW<500 and logP<5, so the binding should be specific enough to the target protein."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "selection = ['canonical_smiles', 'molecule_chembl_id']\n",
    "df_prep = df_pIC50[selection]\n",
    "df_prep.to_csv(\"molecule.smi\",sep=\"\\t\",index=False, header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CC12CCC(O)CC1=CCC1C2CCC2(C)C(CC3CN3)CCC12\tCHEMBL341591\n",
      "C[C@]12CC[C@H]3[C@@H](CC=C4C[C@@H](O)CC[C@@]43C)[C@@H]1CC[C@@H]2[C@H]1CN1\tCHEMBL2111947\n",
      "CCn1c(C(c2ccc(F)cc2)n2ccnc2)c(C)c2cc(Br)ccc21\tCHEMBL431859\n",
      "CCn1cc(C(c2ccc(F)cc2)n2ccnc2)c2ccccc21\tCHEMBL113637\n",
      "Clc1ccccc1Cn1cc(Cn2ccnc2)c2ccccc21\tCHEMBL112021\n",
      "Cc1ccc(S(=O)(=O)n2cc(C(c3ccccc3)n3ccnc3)c3ccccc32)cc1\tCHEMBL324070\n",
      "CCn1ccc2cc(C(c3ccc(F)cc3)n3ccnc3)ccc21\tCHEMBL41761\n",
      "Cn1cc(C(c2ccc(F)cc2)n2ccnc2)c2cc(Br)ccc21\tCHEMBL111868\n",
      "CCn1cc(C(c2ccc(F)cc2)n2ccnc2)c2cc(Br)ccc21\tCHEMBL111888\n",
      "CCn1ccc2cc(C(c3ccccc3)n3ccnc3)ccc21\tCHEMBL112074\n",
      "N#Cc1ccc(Cn2cc(Cn3ccnc3)c3ccccc32)cc1\tCHEMBL324326\n",
      "CCCCCCN1C(=O)CCC(CC)(c2ccncc2)C1=O\tCHEMBL37321\n",
      "c1ccc2c(c1)CCC1C(c3cc[nH]n3)C21\tCHEMBL353068\n",
      "CCCCCCCC1(c2ccncc2)CCC(=O)NC1=O\tCHEMBL41066\n",
      "O=C1/C(=C/c2cccnn2)CCc2ccccc21\tCHEMBL166709\n",
      "O=C1/C(=C/c2ccnnc2)CCc2ccccc21\tCHEMBL424556\n",
      "C[C@]12CC[C@H]3[C@@H](CC=C4[C@H](O)CCC[C@@]43CO)[C@@H]1CCC2=O\tCHEMBL1630273\n",
      "C[C@]12CC[C@H]3[C@@H](C[C@@H](O)C4=CCCC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL1630261\n",
      "O=C1/C(=C\\c2c[nH]cn2)CCc2ccccc21\tCHEMBL169251\n",
      "O=C1/C(=C/c2cccnc2)CCc2ccccc21\tCHEMBL168636\n",
      "O=C1/C(=C/c2ccncc2)CCc2ccccc21\tCHEMBL90585\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=CCCC[C@@]43CO)[C@@H]1CCC2=O\tCHEMBL1629805\n",
      "c1ccc2c(c1)CCC1C(c3ccsc3)C21\tCHEMBL433728\n",
      "CCCN1C(=O)CCC(CC)(c2ccncc2)C1=O\tCHEMBL38877\n",
      "O=C1c2ccccc2CCC1C(O)c1cnccn1\tCHEMBL169449\n",
      "CCCCN1C(=O)CCC(CC)(c2ccncc2)C1=O\tCHEMBL39275\n",
      "CCCCCN1C(=O)CCC(CC)(c2ccncc2)C1=O\tCHEMBL39513\n",
      "CC(=O)O[C@H]1CCC[C@@]2(CO)C1=CC[C@H]1[C@@H]3CCC(=O)[C@@]3(C)CC[C@@H]12\tCHEMBL2112738\n",
      "CCC1(c2ccncc2)CCC(=O)NC1=O\tCHEMBL289116\n",
      "CCC1(c2ccncc2)CCC(=O)NC1=O\tCHEMBL289116\n",
      "CCC1(c2ccncc2)CCC(=O)N(C)C1=O\tCHEMBL39782\n",
      "c1ccc2c(c1)CCC1C(c3ccncc3)C21\tCHEMBL304903\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "O=C1/C(=C/c2c[nH]cn2)CCc2ccccc21\tCHEMBL168434\n",
      "c1ccc2c(c1)CCC1C(c3c[nH]cn3)C21\tCHEMBL352645\n",
      "CC(=O)O[C@H]1CCC[C@@]2(C)C1=CC[C@H]1[C@@H]3CCC(=O)[C@@]3(C)CC[C@@H]12\tCHEMBL2112739\n",
      "c1cncc(C2C3CCc4ccccc4C32)c1\tCHEMBL440930\n",
      "C[C@]12CC[C@H]3[C@@H](CC=C4[C@@H](O)CCC[C@@]43CO)[C@@H]1CCC2=O\tCHEMBL1630274\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=CCCC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL1629804\n",
      "CCCCCCCCN1C(=O)CCC(CC)(c2ccncc2)C1=O\tCHEMBL39661\n",
      "CCCCCCCCN1C(=O)CCC(CC)(c2ccncc2)C1=O\tCHEMBL39661\n",
      "CCCCCCCCCN1C(=O)CCC(CC)(c2ccncc2)C1=O\tCHEMBL38550\n",
      "CCCCCCC1(c2ccncc2)CCC(=O)NC1=O\tCHEMBL39152\n",
      "c1ccc(C2C3CCc4ccccc4C32)nc1\tCHEMBL166789\n",
      "C[C@]12CC[C@H]3[C@@H](CC=C4[C@H](O)CCC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL3349856\n",
      "CO[C@H]1CCC[C@@]2(CO)C1=CC[C@H]1[C@@H]3CCC(=O)[C@@]3(C)CC[C@@H]12\tCHEMBL1630275\n",
      "C[C@]12CC[C@H]3[C@@H](C[C@@H](O)C4=CCCC[C@@]43CO)[C@@H]1CCC2=O\tCHEMBL1630267\n",
      "CCCCCCCN1C(=O)CCC(CC)(c2ccncc2)C1=O\tCHEMBL38439\n",
      "CC1(c2ccncc2)CCC(=O)NC1=O\tCHEMBL38510\n",
      "O=C1/C(=C/c2cc[nH]n2)CCc2ccccc21\tCHEMBL415753\n",
      "C[C@]12CC[C@H]3[C@@H](CC=C4[C@@H](O)CCC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL3349864\n",
      "C[C@]12CC[C@H]3[C@@H](C[C@H](O)C4=CCCC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL1629806\n",
      "O=C1c2ccccc2CCC1C(O)c1cccnn1\tCHEMBL169664\n",
      "O=C1c2ccccc2CCC1C(O)c1ccnnc1\tCHEMBL168762\n",
      "CCCC1(c2ccncc2)CCC(=O)NC1=O\tCHEMBL275594\n",
      "CCN1C(=O)CCC(CC)(c2ccncc2)C1=O\tCHEMBL39781\n",
      "O=C1/C(=C/c2cnccn2)CCc2ccccc21\tCHEMBL413344\n",
      "O=C1c2ccccc2CCC1C(O)c1ccncn1\tCHEMBL168444\n",
      "CCCCCC1(c2ccncc2)CCC(=O)NC1=O\tCHEMBL288712\n",
      "O=C1/C(=C/c2cncs2)CCc2ccccc21\tCHEMBL169086\n",
      "c1ccc(C2C3CCc4ccccc4C32)cc1\tCHEMBL168700\n",
      "Nc1ncc(/C=C2\\CCc3ccccc3C2=O)s1\tCHEMBL355681\n",
      "CCCCCCCCCC1(c2ccncc2)CCC(=O)NC1=O\tCHEMBL289766\n",
      "C[C@]12CC[C@H]3[C@@H](C[C@H](O)C4=CCCC[C@@]43CO)[C@@H]1CCC2=O\tCHEMBL1630266\n",
      "CC(=O)O[C@@H]1CCC[C@@]2(C)C1=CC[C@H]1[C@@H]3CCC(=O)[C@@]3(C)CC[C@@H]12\tCHEMBL2111917\n",
      "CCCCCCCCCCN1C(=O)CCC(CC)(c2ccncc2)C1=O\tCHEMBL39840\n",
      "O=C1/C(=C/c2ccccn2)CCc2ccccc21\tCHEMBL424203\n",
      "C[C@]12CC[C@H]3[C@@H](CC=C4CCCC[C@@]43CO)[C@@H]1CCC2=O\tCHEMBL1630911\n",
      "CCCCC1(c2ccncc2)CCC(=O)NC1=O\tCHEMBL40125\n",
      "O=C1/C(=C/c2ccccc2)CCc2ccccc21\tCHEMBL28689\n",
      "O=C1/C(=C/c2ccsc2)CCc2ccccc21\tCHEMBL166974\n",
      "C[C@]12CC[C@H]3[C@@H](CC=C4CCCC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL2311169\n",
      "CCCCCCCCC1(c2ccncc2)CCC(=O)NC1=O\tCHEMBL38472\n",
      "CCCCCCCCC1(c2ccncc2)CCC(=O)NC1=O\tCHEMBL38472\n",
      "CSc1cc2c(cc1C(F)(F)F)N(C(=O)Nc1cccnc1)CC2\tCHEMBL276140\n",
      "Cn1ccc2cc3c(cc21)CCN3C(=O)Nc1cccnc1\tCHEMBL297784\n",
      "Cc1cc2c(cc1Cl)N(C(=O)Nc1ccc(Oc3cccnc3C)nc1)CC2\tCHEMBL14563\n",
      "O=C1CC(c2ccccc2)Oc2ccccc21\tCHEMBL274318\n",
      "C#CC[C@H]1CC2C(CCC3(C)C(=O)CCC23)C2CCC(=O)C=C21\tCHEMBL113739\n",
      "C#CC[C@H]1CC2C3CCC(=O)C3(C)CCC2C2(C)CCC(=O)C=C12\tCHEMBL113121\n",
      "COc1ccc2c(c1)OC(c1ccccc1)/C(=C\\c1ccncc1)C2=O\tCHEMBL10137\n",
      "O=C1/C(=C\\c2ccncc2)C(c2ccccc2)Oc2ccccc21\tCHEMBL10072\n",
      "C#CC[C@@H]1CC2C3CCC(=O)C3(C)CCC2C2(C)CCC(=O)C=C12\tCHEMBL113082\n",
      "COc1ccc2c(c1)OC(c1ccccc1)CC2=O\tCHEMBL9687\n",
      "O=C1/C(=C/c2ccncc2)C(c2ccccc2)Oc2ccccc21\tCHEMBL10026\n",
      "C#CC[C@]12CCC(=O)C=C1CC[C@H]1[C@@H]3CCC(=O)[C@@]3(C)CC[C@@H]12\tCHEMBL2105261\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "C#CC[C@@H]1CC2C(CCC3(C)C(=O)CCC23)C2CCC(=O)C=C21\tCHEMBL109211\n",
      "COc1ccc2c(c1)OC(c1ccccc1)/C(=C/c1ccncc1)C2=O\tCHEMBL267508\n",
      "CCCCCCCCCCN1C(=O)C2CC(c3ccc(N)cc3)(C2)C1=O\tCHEMBL27879\n",
      "Nc1ccc([C@@]23C[C@@H]2C(=O)NC3=O)cc1\tCHEMBL27386\n",
      "Nc1ccc(C23CC2C(=O)NC3=O)cc1\tCHEMBL287574\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "Nc1ccc(C23CC2C(=O)N(CC2CCCCC2)C3=O)cc1\tCHEMBL281947\n",
      "Nc1ccc(C23CC2C(=O)N(CC2CCCCC2)C3=O)cc1\tCHEMBL281947\n",
      "Nc1ccc(C23CC(C2)C(=O)NC3=O)cc1\tCHEMBL26543\n",
      "Nc1ccc(C23CC(C2)C(=O)N(CC2CCCCC2)C3=O)cc1\tCHEMBL27546\n",
      "CC(C)CN1C(=O)C2CC(c3ccc(N)cc3)(C2)C1=O\tCHEMBL286506\n",
      "Nc1ccc(C23CC2C(=O)N(CC2CCCCC2)C3=O)cc1\tCHEMBL281947\n",
      "Nc1ccc(C23CC2C(=O)N(CC2CCCCC2)C3=O)cc1\tCHEMBL281947\n",
      "Nc1ccc(C23CC2C(=O)N(CC2CCCCC2)C3=O)cc1\tCHEMBL281947\n",
      "CCCCCN1C(=O)C2CC2(c2ccc(N)cc2)C1=O\tCHEMBL26651\n",
      "CCCCCN1C(=O)C2CC(c3ccc(N)cc3)(C2)C1=O\tCHEMBL27591\n",
      "CCCCCCCN1C(=O)C2CC(c3ccc(N)cc3)(C2)C1=O\tCHEMBL28173\n",
      "COc1ccc(CN2C(=O)C3CC(c4ccc(N)cc4)(C3)C2=O)cc1\tCHEMBL28290\n",
      "Nc1ccc(C23CC(C2)C(=O)N(C2CCCCC2)C3=O)cc1\tCHEMBL27467\n",
      "Nc1ccc(C23CC(C2)C(=O)N(C2CCCCC2)C3=O)cc1\tCHEMBL27467\n",
      "CCCN1C(=O)C2CC(c3ccc(N)cc3)(C2)C1=O\tCHEMBL27698\n",
      "Nc1ccc([C@]23C[C@H]2C(=O)NC3=O)cc1\tCHEMBL27767\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2ccnc2)cc1\tCHEMBL29482\n",
      "CC(=O)OCC12CCCC=C1CCC1C2CCC2(C)C(OC(C)=O)CCC12\tCHEMBL311077\n",
      "C#CCSCC[C@]12CCC(=O)C=C1CCC1C2=CC[C@]2(C)C(=O)CCC12\tCHEMBL424162\n",
      "CCSC(C[C@]12CCC(=O)C=C1CCC1C2=CC[C@]2(C)C(=O)CCC12)SCC\tCHEMBL349514\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=CCCC[C@@]43C(F)F)[C@@H]1CC[C@@H]2O\tCHEMBL2112309\n",
      "CSCC[C@]12CCC(=O)C=C1CCC1C2=CC[C@]2(C)C(=O)CCC12\tCHEMBL349704\n",
      "C[C@]12CC=C3C(CCC4=CC(=O)CC[C@@]43CCSCCS)C1CCC2=O\tCHEMBL165279\n",
      "CC(=O)OCC12CCCC=C1CCC1C2CCC2(C)C(O)CCC12\tCHEMBL443530\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "CSSCC[C@]12CCC(=O)C=C1CCC1C2=CC[C@]2(C)C(=O)CCC12\tCHEMBL352891\n",
      "CC(=O)OC1CCC2C3CCC4=CCCCC4(CO)C3CCC12C\tCHEMBL81255\n",
      "C=C(c1ccc(C#N)cc1)n1ccnc1\tCHEMBL31760\n",
      "CC(=O)OCC12CCCC=C1CCC1C3CCC(=O)C3(C)CCC12\tCHEMBL309656\n",
      "CC12CCC3C(CCC4=CCCCC43C(F)F)C1CCC2=O\tCHEMBL83835\n",
      "CC(=O)SCC[C@]12CCC(=O)C=C1CCC1C2=CC[C@]2(C)C(=O)CCC12\tCHEMBL349919\n",
      "C[C@]12CC=C3C(CCC4=CC(=O)CC[C@@]43CCSc3ccccc3)C1CCC2=O\tCHEMBL349963\n",
      "CSCSCC[C@]12CCC(=O)C=C1CCC1C2=CC[C@]2(C)C(=O)CCC12\tCHEMBL353571\n",
      "CSCCC[C@]12CCC(=O)C=C1CCC1C2=CC[C@]2(C)C(=O)CCC12\tCHEMBL164746\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=CCCC[C@@]43CO)[C@@H]1CCC2=O\tCHEMBL1629805\n",
      "CN(C)CC[C@]12CCC(=O)C=C1CCC1C2=CC[C@]2(C)C(=O)CCC12\tCHEMBL166017\n",
      "C[C@]12CC=C3C(CCC4=CC(=O)CC[C@@]43CC[Se]c3ccccc3)C1CCC2=O\tCHEMBL164821\n",
      "CSC(=O)C[C@]12CCC(=O)C=C1CCC1C3CCC(=O)[C@@]3(C)CCC12\tCHEMBL164993\n",
      "COCC[C@]12CCC(=O)C=C1CCC1C2=CC[C@]2(C)C(=O)CCC12\tCHEMBL165925\n",
      "CCSCC[C@]12CCC(=O)C=C1CCC1C2=CC[C@]2(C)C(=O)CCC12\tCHEMBL165218\n",
      "C#CSCC[C@]12CCC(=O)C=C1CCC1C2=CC[C@]2(C)C(=O)CCC12\tCHEMBL350713\n",
      "CC12CCC3C(CCC4=CCCCC43C)C1C[C@H](Br)C2=O\tCHEMBL81134\n",
      "N#Cc1ccc(/C(=N\\c2ccc(Br)cc2)n2ccnc2)cc1\tCHEMBL31456\n",
      "N#Cc1ccc([C@@H]2CCCc3cncn32)cc1\tCHEMBL31215\n",
      "CSCC[C@]12CCC(=O)C=C1CCC1C3CCC(=O)[C@@]3(C)CCC12\tCHEMBL350074\n",
      "C[C@]12CC=C3C(CCC4=CC(=O)CC[C@@]43CCSC#N)C1CCC2=O\tCHEMBL166032\n",
      "C=CSCC[C@]12CCC(=O)C=C1CCC1C2=CC[C@]2(C)C(=O)CCC12\tCHEMBL164543\n",
      "C#CC(O)C12CCCC=C1CCC1C3CCC(=O)C3(C)CCC12\tCHEMBL82981\n",
      "C[C@]12CC=C3C(CCC4=CC(=O)CC[C@@]43CCSSc3ccc([N+](=O)[O-])cc3[N+](=O)[O-])C1CCC2=O\tCHEMBL165552\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=CCCC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL1629804\n",
      "N#Cc1ccc(C2(c3ccc(C#N)cc3)CCCc3cncn32)cc1\tCHEMBL285809\n",
      "C[C@]12CC=C3C(CCC4=CC(=O)CC[C@@]43CCSCCCl)C1CCC2=O\tCHEMBL351850\n",
      "C[C@]12CC=C3C(CCC4=CC(=O)CC[C@@]43CCSC(F)F)C1CCC2=O\tCHEMBL166148\n",
      "N#Cc1ccc([C@H]2CCCc3cncn32)cc1\tCHEMBL287677\n",
      "N#Cc1ccc2c(c1)CCC=C2n1ccnc1\tCHEMBL31224\n",
      "C=CCSCC[C@]12CCC(=O)C=C1CCC1C2=CC[C@]2(C)C(=O)CCC12\tCHEMBL351526\n",
      "CSC(C[C@]12CCC(=O)C=C1CCC1C2=CC[C@]2(C)C(=O)CCC12)SC\tCHEMBL351226\n",
      "C[C@]12CC=C3C(CCC4=CC(=O)CC[C@@]43CCSSc3ccccc3)C1CCC2=O\tCHEMBL349851\n",
      "CC12CCC3C(CCC4=CCCCC43CO)C1CCC2O\tCHEMBL440691\n",
      "CC12CCC3C(CCC4=CCCCC43C)C1C[C@@H](Br)C2=O\tCHEMBL83422\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=C(O)C(=O)CC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL132530\n",
      "C[C@]12CC=C3C(CCC4=CC(=O)CC[C@@]43CCI)C1CCC2=O\tCHEMBL166060\n",
      "CC12CCCC=C1CCC1C2CCC2(C)C(O)CCC12\tCHEMBL439950\n",
      "C[C@]12CC=C3C(CCC4=CC(=O)CC[C@@]43CCSCF)C1CCC2=O\tCHEMBL165106\n",
      "C[C@]12CC=C3C(CCC4=CC(=O)CC[C@@]43CCSCCO)C1CCC2=O\tCHEMBL423096\n",
      "C[C@]12CC=C3C(CCC4=CC(=O)CC[C@@]43CCOS(C)(=O)=O)C1CCC2=O\tCHEMBL424185\n",
      "C[C@]12CC=C3C(CCC4=CC(=O)CC[C@@]43CCSC3CC3)C1CCC2=O\tCHEMBL166079\n",
      "CS[C@]12CCC(=O)C=C1CCC1C2=CC[C@]2(C)C(=O)CCC12\tCHEMBL165342\n",
      "O=C1c2cccc(O)c2CCC1Cc1ccncc1\tCHEMBL93828\n",
      "O=C1/C(=C/c2ccncc2)CCc2cc(O)ccc21\tCHEMBL91992\n",
      "COc1ccc2c(c1)C(=O)/C(=C/c1ccncc1)CC2\tCHEMBL419131\n",
      "O=C1/C(=C/c2ccncc2)CCc2ccc(O)cc21\tCHEMBL313774\n",
      "O=C1c2ccc(O)cc2CCC1Cc1ccncc1\tCHEMBL91818\n",
      "O=C1/C(=C/c2ccncc2)CCc2ccccc21\tCHEMBL90585\n",
      "COc1ccc2c(c1)CCC(Cc1ccncc1)C2=O\tCHEMBL88233\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "O=C1c2cc(O)ccc2CCC1Cc1ccncc1\tCHEMBL90077\n",
      "COc1ccc2c(c1)C(=O)C(Cc1ccncc1)CC2\tCHEMBL93666\n",
      "O=C1c2cccc(O)c2CCC1Cc1ccncc1\tCHEMBL93828\n",
      "COc1cccc2c1CC/C(=C\\c1ccncc1)C2=O\tCHEMBL91101\n",
      "COc1ccc2c(c1)CC/C(=C\\c1ccncc1)C2=O\tCHEMBL328217\n",
      "COc1ccc2c(c1)C(=O)C(Cc1ccncc1)CC2\tCHEMBL93666\n",
      "O=C1c2cccc(O)c2CCC1Cc1ccncc1\tCHEMBL93828\n",
      "O=C1c2ccccc2CCC1Cc1ccncc1\tCHEMBL92065\n",
      "COc1ccc2c(c1)C(=O)C(Cc1ccncc1)CC2\tCHEMBL93666\n",
      "O=C1/C(=C/c2ccncc2)CCc2c(O)cccc21\tCHEMBL92488\n",
      "COc1cccc2c1CCC(Cc1ccncc1)C2=O\tCHEMBL90427\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=CCCC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL1629804\n",
      "CCCCC[C@H]1CC2C(CCC3(C)C(O)CCC23)C2(C)CCCC=C12\tCHEMBL307006\n",
      "C[C@@H]1CC2C(CCC3(C)C(O)CCC23)C2(C)CCCC=C12\tCHEMBL73367\n",
      "CCCCC[C@H]1CC2C3CCC(=O)C3(C)CCC2C2(C)CCCC=C12\tCHEMBL305630\n",
      "CCC[C@@H]1CC2C3CCC(=O)C3(C)CCC2C2(C)CCCC=C12\tCHEMBL70317\n",
      "CC[C@@H]1CC2C3CCC(=O)C3(C)CCC2C2(C)CCCC=C12\tCHEMBL307690\n",
      "CC12CCCC=C1CCC1C2CCC2(C)C1CC[C@@H]2O\tCHEMBL73279\n",
      "CCC[C@H]1CC2C(CCC3(C)C(O)CCC23)C2(C)CCCC=C12\tCHEMBL69891\n",
      "CCCCCCCC[C@@H]1CC2C(CCC3(C)C(O)CCC23)C2(C)CCCC=C12\tCHEMBL72056\n",
      "CC12CCC3C(C[C@H](O)C4=CCCCC43C)C1CCC2=O\tCHEMBL307643\n",
      "C[C@H]1CC2C3CCC(=O)C3(C)CCC2C2(C)CCCC=C12\tCHEMBL310735\n",
      "CCCCCCCC[C@H]1CC2C(CCC3(C)C(O)CCC23)C2(C)CCCC=C12\tCHEMBL73368\n",
      "CO[C@@H]1CC2C3CCC(=O)C3(C)CCC2C2(C)CCCC=C12\tCHEMBL308537\n",
      "CO[C@H]1CC2C3CCC(=O)C3(C)CCC2C2(C)CCCC=C12\tCHEMBL307581\n",
      "C[C@H]1CC2C(CCC3(C)C(O)CCC23)C2(C)CCCC=C12\tCHEMBL70959\n",
      "C[C@@H]1CC2C3CCC(=O)C3(C)CCC2C2(C)CCCC=C12\tCHEMBL305205\n",
      "CCC[C@@H]1CC2C(CCC3(C)C(O)CCC23)C2(C)CCCC=C12\tCHEMBL73071\n",
      "CCCCC[C@@H]1CC2C3CCC(=O)C3(C)CCC2C2(C)CCCC=C12\tCHEMBL72415\n",
      "CCCCCCCC[C@H]1CC2C3CCC(=O)C3(C)CCC2C2(C)CCCC=C12\tCHEMBL74295\n",
      "CC[C@H]1CC2C3CCC(=O)C3(C)CCC2C2(C)CCCC=C12\tCHEMBL70436\n",
      "CCCCCCCC[C@@H]1CC2C3CCC(=O)C3(C)CCC2C2(C)CCCC=C12\tCHEMBL72538\n",
      "CC[C@@H]1CC2C(CCC3(C)C(O)CCC23)C2(C)CCCC=C12\tCHEMBL306022\n",
      "CC(=O)O[C@H]1CC2C3CCC(=O)C3(C)CCC2C2(C)CCCC=C12\tCHEMBL72228\n",
      "CC12CCC3C(C[C@@H](O)C4=CCCCC43C)C1CCC2=O\tCHEMBL73993\n",
      "CC(=O)O[C@@H]1CC2C3CCC(=O)C3(C)CCC2C2(C)CCCC=C12\tCHEMBL304770\n",
      "CCCCC[C@@H]1CC2C(CCC3(C)C(O)CCC23)C2(C)CCCC=C12\tCHEMBL73219\n",
      "CC[C@H]1CC2C(CCC3(C)C(O)CCC23)C2(C)CCCC=C12\tCHEMBL74339\n",
      "CCC[C@H]1CC2C3CCC(=O)C3(C)CCC2C2(C)CCCC=C12\tCHEMBL304305\n",
      "N#Cc1ccc(N(Cc2ccc(OS(N)(=O)=O)cc2)n2cnnc2)cc1\tCHEMBL430879\n",
      "N#Cc1ccc(N(Cc2cccc(OS(N)(=O)=O)c2)n2cnnc2)cc1\tCHEMBL325346\n",
      "N#Cc1ccc(N(Cc2ccc(OS(N)(=O)=O)c(Cl)c2)n2cnnc2)cc1\tCHEMBL322322\n",
      "N#Cc1ccc(N(Cc2ccc(OS(N)(=O)=O)c(Br)c2)n2cnnc2)cc1\tCHEMBL108483\n",
      "C[C@]12CCC3C(CCC4=C(O)C(=O)CC[C@@]43C(F)F)C1CCC2=O\tCHEMBL99057\n",
      "N#Cc1ccc(N(Cc2ccc(Br)cc2)n2cnnc2)cc1\tCHEMBL108425\n",
      "Clc1ccc(COC(Cn2cccc2)c2ccc(Cl)cc2Cl)cc1\tCHEMBL317581\n",
      "N#Cc1ccc(N(Cc2ccc(OS(N)(=O)=O)c(F)c2)n2cnnc2)cc1\tCHEMBL108646\n",
      "C[C@@H]1C(OC(=O)Cc2ccccn2)CC2CC1C2(C)C\tCHEMBL134748\n",
      "C[C@@H]1CCC2C(C)(C)C3CC21CC[C@@]3(C)OC(=O)Cc1ccncc1\tCHEMBL134442\n",
      "C[C@@H]1CCC2C(C)(C)C3CC21CC[C@@]3(C)OC(=O)Cc1cccnc1\tCHEMBL135095\n",
      "CC(C(=O)OC12CC3CC(CC(C3)C1)C2)c1cccnc1\tCHEMBL134509\n",
      "CC(C)(C(=O)OC1(C)C2CC3CC(C2)CC1C3)c1cccnc1\tCHEMBL3084923\n",
      "CC(C)(C(=O)OC12CC3CC(CC(C3)C1)C2)c1cccnc1\tCHEMBL134169\n",
      "C[C@@H]1C(OC(=O)Cc2cccnc2)CC2CC1C2(C)C\tCHEMBL131984\n",
      "C[C@@H]1C(OC(=O)Cc2ccncc2)CC2CC1C2(C)C\tCHEMBL132072\n",
      "O=C(Cc1cccnc1)OC12CC3CC(CC(C3)C1)C2\tCHEMBL135037\n",
      "C[C@@H]1C(OC(=O)C(C)(C)c2cccnc2)CC2CC1C2(C)C\tCHEMBL337251\n",
      "CC1(OC(=O)Cc2cccnc2)C2CC3CC(C2)CC1C3\tCHEMBL3084924\n",
      "CCC(CC)(C(=O)OC1CC2CC([C@@H]1C)C2(C)C)c1ccncc1\tCHEMBL132380\n",
      "C[C@@H]1CCC2C(C)(C)C3CC21CC[C@@]3(C)OC(=O)C(C)(C)c1cccnc1\tCHEMBL444068\n",
      "CC(C(=O)OC1CC2CC([C@@H]1C)C2(C)C)c1ccncc1\tCHEMBL133724\n",
      "C[C@@H]1C(OC(=O)C(C)(C)c2ccncc2)CC2CC1C2(C)C\tCHEMBL135070\n",
      "CCC(C(=O)OC1CC2CC([C@@H]1C)C2(C)C)c1ccncc1\tCHEMBL336102\n",
      "CC(C(=O)OC1CC2CC([C@@H]1C)C2(C)C)c1cccnc1\tCHEMBL132066\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "CCN1CCc2cccc(C(c3ccc(F)cc3)n3cncn3)c21\tCHEMBL41429\n",
      "Fc1ccc(C(C2Cc3ccccc3O2)n2cncn2)cc1\tCHEMBL63297\n",
      "CCn1ccc2cccc(C(c3ccc(F)cc3)n3ccnc3)c21\tCHEMBL416616\n",
      "COc1ccc2c(c1)C1C(CC2)C1c1ccncc1\tCHEMBL294361\n",
      "Cc1ccc(C(C2Cc3ccccc3O2)n2cncn2)cc1\tCHEMBL65328\n",
      "COc1ccc2c(c1)C1C(CC2)C1c1ccncc1\tCHEMBL294361\n",
      "COc1ccc(Br)c2c1CCC1C(c3ccncc3)C21\tCHEMBL63336\n",
      "CCn1ccc2cc(C(c3ccc(F)cc3)n3ccnc3)ccc21\tCHEMBL41761\n",
      "Oc1ccc2c(c1)CCC1C(c3ccncc3)C21\tCHEMBL304222\n",
      "CCn1ccc2cc(Br)cc(C(c3ccc(F)cc3)n3ccnc3)c21\tCHEMBL39423\n",
      "COc1cc2c(cc1Br)CCC1C(c3ccncc3)C21\tCHEMBL419619\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "CCN1CCc2cccc(C(c3cccc(Cl)c3)n3ccnc3)c21\tCHEMBL288695\n",
      "COc1cccc2c1CCC1C(c3ccncc3)C21\tCHEMBL66673\n",
      "Clc1ccc(C(C2Cc3ccccc3O2)n2cncn2)cc1\tCHEMBL291646\n",
      "CCN1CCc2cc(Br)cc(C(c3ccc(F)cc3)n3ccnc3)c21\tCHEMBL287950\n",
      "CCN1CCc2cccc(C(c3ccc(F)cc3)n3ccnc3)c21\tCHEMBL39403\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "CCn1ccc2cccc(C(c3ccc(Br)cc3)n3ccnc3)c21\tCHEMBL285193\n",
      "COc1ccc2c(c1)CCC1C(c3ccncc3)C21\tCHEMBL67842\n",
      "Clc1ccc(C(C2Cc3ccccc3O2)n2cncn2)c(Cl)c1\tCHEMBL304788\n",
      "Fc1ccc(C(C2Cc3ccccc3O2)n2cnnn2)cc1\tCHEMBL294360\n",
      "CCn1ccc2cccc(C(c3cccc(Cl)c3)n3ccnc3)c21\tCHEMBL287967\n",
      "Oc1cccc2c1CCC1C(c3ccncc3)C21\tCHEMBL65590\n",
      "Oc1ccc2c(c1)C1C(CC2)C1c1ccncc1\tCHEMBL304583\n",
      "COc1ccc2c(c1)C1C(CC2)C1c1ccncc1\tCHEMBL294361\n",
      "COc1ccc2c(c1)C1C(CC2)C1(C)c1ccncc1\tCHEMBL66867\n",
      "CCn1ccc2cccc(C(c3ccc(Cl)cc3)n3ccnc3)c21\tCHEMBL289945\n",
      "CCN1CCc2cc(Cl)cc(C(c3ccc(F)cc3)n3ccnc3)c21\tCHEMBL288804\n",
      "COc1cc2c(cc1Br)CCC1C(c3ccncc3)C21\tCHEMBL419619\n",
      "Clc1ccc(C(C2Cc3ccccc3O2)n2cnnn2)c(Cl)c1\tCHEMBL65549\n",
      "Clc1ccc(C(C2Cc3ccccc3O2)n2cnnn2)cc1\tCHEMBL294357\n",
      "COc1cc2c(cc1Br)CCC1C(c3ccncc3)C21\tCHEMBL419619\n",
      "c1ccc2c(c1)CCC1C(c3ccncc3)C21\tCHEMBL304903\n",
      "CC12CCC3C(Cc4c5c(nn4C/C=C/c4ccccc4)C(=O)CCC53C)C1CCC2=O\tCHEMBL422610\n",
      "CC12CCC3C(Cc4nn(C/C=C/c5ccccc5)c5c4C3(C)CCC5=O)C1CCC2=O\tCHEMBL131414\n",
      "CC12CCC3C(Cc4c5c(nn4Cc4ccc([N+](=O)[O-])cc4)C(=O)CCC53C)C1CCC2=O\tCHEMBL129883\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=C(O)C(=O)CC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL132530\n",
      "CC12CCC3C(Cc4nn(Cc5ccc([N+](=O)[O-])cc5)c5c4C3(C)CCC5=O)C1CCC2=O\tCHEMBL336383\n",
      "CC12CCC3C(Cc4nn(Cc5ccccc5)c5c4C3(C)CCC5=O)C1CCC2=O\tCHEMBL133240\n",
      "CC12CCC3C(Cc4n[nH]c5c4C3(C)CCC5=O)C1CCC2=O\tCHEMBL132563\n",
      "CC12CCC3C(Cc4c5c(nn4Cc4ccccc4)C(=O)CCC53C)C1CCC2=O\tCHEMBL130546\n",
      "C=CCN1C(=O)C2CC2(c2ccc(N)cc2)C1=O\tCHEMBL176452\n",
      "C=CCN1C(=O)C2CC2(c2ccc(N)cc2)C1=O\tCHEMBL176452\n",
      "CC(C)N1C(=O)C2CC2(c2ccc(N)cc2)C1=O\tCHEMBL172263\n",
      "CC(C)N1C(=O)C2CC2(c2ccc(N)cc2)C1=O\tCHEMBL172263\n",
      "CC(C)CN1C(=O)C2CC2(c2ccc(N)cc2)C1=O\tCHEMBL174308\n",
      "CCCN1C(=O)C2CC2(c2ccc(N)cc2)C1=O\tCHEMBL174407\n",
      "CCCN1C(=O)C2CC2(c2ccc(N)cc2)C1=O\tCHEMBL174407\n",
      "CC(C)(C)CN1C(=O)C2CC2(c2ccc(N)cc2)C1=O\tCHEMBL368631\n",
      "CC(C)(C)CN1C(=O)C2CC2(c2ccc(N)cc2)C1=O\tCHEMBL368631\n",
      "CCCCCN1C(=O)C2CC2(c2ccc(N)cc2)C1=O\tCHEMBL26651\n",
      "CCCCCN1C(=O)C2CC2(c2ccc(N)cc2)C1=O\tCHEMBL26651\n",
      "Nc1ccc(C23CC2C(=O)NC3=O)cc1\tCHEMBL287574\n",
      "Nc1ccc(C23CC2C(=O)NC3=O)cc1\tCHEMBL287574\n",
      "Nc1ccc(C23CC2C(=O)N(Cc2ccccc2)C3=O)cc1\tCHEMBL369603\n",
      "Nc1ccc(C23CC2C(=O)N(Cc2ccccc2)C3=O)cc1\tCHEMBL369603\n",
      "CCCCN1C(=O)C2CC2(c2ccc(N)cc2)C1=O\tCHEMBL174909\n",
      "CCCCN1C(=O)C2CC2(c2ccc(N)cc2)C1=O\tCHEMBL174909\n",
      "CCN1C(=O)C2CC2(c2ccc(N)cc2)C1=O\tCHEMBL354401\n",
      "CCN1C(=O)C2CC2(c2ccc(N)cc2)C1=O\tCHEMBL354401\n",
      "C#CCN1C(=O)C2CC2(c2ccc(N)cc2)C1=O\tCHEMBL176539\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "CN1C(=O)C2CC2(c2ccc(N)cc2)C1=O\tCHEMBL368732\n",
      "CN1C(=O)C2CC2(c2ccc(N)cc2)C1=O\tCHEMBL368732\n",
      "CCCCCCC[C@@H]1CC2C(CCC3(C)C(=O)CCC23)C2(C)C=CC(=O)C=C12\tCHEMBL268918\n",
      "CCCCCCC[C@H]1CC2C(CCC3(C)C(=O)CCC23)C2(C)C=CC(=O)C=C12\tCHEMBL268209\n",
      "CCC[C@H]1CC2C(CCC3(C)C(=O)CCC23)C2(C)C=CC(=O)C=C12\tCHEMBL8333\n",
      "CCCCCC[C@H]1CC2C(CCC3(C)C(=O)CCC23)C2(C)C=CC(=O)C=C12\tCHEMBL266532\n",
      "C[C@]12C=CC(=O)C=C1CC[C@@H]1[C@@H]2CC[C@]2(C)C(=O)CC[C@@H]12\tCHEMBL1078534\n",
      "CCCCC[C@H]1CC2C(CCC3(C)C(=O)CCC23)C2(C)C=CC(=O)C=C12\tCHEMBL267668\n",
      "CCC[C@@H]1CC2C(CCC3(C)C(=O)CCC23)C2(C)C=CC(=O)C=C12\tCHEMBL8254\n",
      "CCCC[C@@H]1CC2C(CCC3(C)C(=O)CCC23)C2(C)C=CC(=O)C=C12\tCHEMBL8417\n",
      "C[C@]12C=CC(=O)C=C1CC[C@@H]1[C@@H]2CC[C@]2(C)C(=O)CC[C@@H]12\tCHEMBL1078534\n",
      "C[C@@H]1CC2C(CCC3(C)C(=O)CCC23)C2(C)C=CC(=O)C=C12\tCHEMBL8315\n",
      "C[C@H]1CC2C(CCC3(C)C(=O)CCC23)C2(C)C=CC(=O)C=C12\tCHEMBL274878\n",
      "CCCC[C@H]1CC2C(CCC3(C)C(=O)CCC23)C2(C)C=CC(=O)C=C12\tCHEMBL8646\n",
      "CC[C@H]1CC2C(CCC3(C)C(=O)CCC23)C2(C)C=CC(=O)C=C12\tCHEMBL8498\n",
      "CC[C@@H]1CC2C(CCC3(C)C(=O)CCC23)C2(C)C=CC(=O)C=C12\tCHEMBL8313\n",
      "CCCCC[C@@H]1CC2C(CCC3(C)C(=O)CCC23)C2(C)C=CC(=O)C=C12\tCHEMBL440832\n",
      "CCCCCC[C@@H]1CC2C(CCC3(C)C(=O)CCC23)C2(C)C=CC(=O)C=C12\tCHEMBL266350\n",
      "C[C@]12CC[C@H]3[C@@H](CC=C4C[C@@H](O)CC[C@@]43C)[C@@H]1CC=C2c1ccccn1\tCHEMBL2112301\n",
      "COc1cccc2c1C/C(=C/c1ccncc1)C2=O\tCHEMBL23334\n",
      "Oc1ccc2c(c1)CC(Cc1ccncc1)CC2\tCHEMBL23394\n",
      "COc1cccc2c1CC(Cc1ccncc1)C2=O\tCHEMBL283782\n",
      "COc1ccc2c(c1)CC(Cc1ccncc1)C2=O\tCHEMBL26279\n",
      "c1ccc2c(c1)CCC(Cc1ccncc1)C2\tCHEMBL24233\n",
      "COc1ccc2c(c1)C/C(=C\\c1ccncc1)C2=O\tCHEMBL442262\n",
      "COc1cccc2c1C/C(=C\\c1ccncc1)C2=O\tCHEMBL288093\n",
      "Oc1cccc2c1CC(Cc1ccncc1)C2\tCHEMBL23485\n",
      "COc1cccc2c1CC(Cc1ccncc1)C2\tCHEMBL24115\n",
      "C[C@]12CCC3C(CCC4=CC(=O)CC[C@@]43C)C1CC=C2c1cccnc1\tCHEMBL432751\n",
      "COc1ccc2c(c1)CC(Cc1ccncc1)C2\tCHEMBL23646\n",
      "Oc1ccc2c(c1)CCC(Cc1ccncc1)C2\tCHEMBL279940\n",
      "COc1ccc2c(c1)C/C(=C/c1ccncc1)C2=O\tCHEMBL285155\n",
      "O=C1c2ccccc2CC1Cc1ccncc1\tCHEMBL23484\n",
      "O=C1/C(=C\\c2ccncc2)Cc2ccccc21\tCHEMBL285973\n",
      "Oc1cccc2c1CCC(Cc1ccncc1)C2\tCHEMBL23364\n",
      "C[C@]12CCC3C(CC[C@H]4CC(=O)CC[C@]34C)C1CC=C2c1cccnc1\tCHEMBL84230\n",
      "Oc1ccc2c(c1)CC(Cc1ccncc1)C2\tCHEMBL24014\n",
      "COc1cccc2c1CCC(Cc1ccncc1)C2\tCHEMBL23148\n",
      "C[C@]12CC[C@H]3[C@@H](CC=C4C[C@@H](O)CC[C@@]43C)[C@@H]1CC=C2c1ccncc1\tCHEMBL2112299\n",
      "O=C1c2cccc(O)c2CC1Cc1ccncc1\tCHEMBL22485\n",
      "COc1ccc2c(c1)CC(Cc1ccncc1)CC2\tCHEMBL23365\n",
      "c1ccc2c(c1)CC(Cc1ccncc1)C2\tCHEMBL24593\n",
      "O=C1/C(=C/c2ccncc2)Cc2c(O)cccc21\tCHEMBL284934\n",
      "O=C1/C(=C/c2ccncc2)Cc2cc(O)ccc21\tCHEMBL26065\n",
      "COc1ccc2c(c1)CCC(Cc1ccncc1)C2\tCHEMBL23097\n",
      "C[C@]12CC[C@H]3[C@@H](CC=C4C[C@@H](O)CC[C@@]43C)[C@@H]1CC=C2c1cccnc1\tCHEMBL254328\n",
      "O=C1/C(=C/c2ccncc2)Cc2ccccc21\tCHEMBL23150\n",
      "O=C1c2ccc(O)cc2CC1Cc1ccncc1\tCHEMBL24279\n",
      "c1cnc2c(c1)C(CCCn1ccnc1)CCC2\tCHEMBL301106\n",
      "O=c1c2ccccc2oc2c(Cn3ccnc3)ccc([N+](=O)[O-])c12\tCHEMBL159073\n",
      "O=c1ccoc2c(Cn3ccnc3)ccc([N+](=O)[O-])c12\tCHEMBL351167\n",
      "C[C@]12CCC3C(CCC4=C(S)C(=O)CC[C@@]43C)C1CCC2=O\tCHEMBL288242\n",
      "N#CCCCC1CCCc2cncn21\tCHEMBL163420\n",
      "c1cc(CCn2ccnc2)c2ccsc2c1\tCHEMBL460382\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=C(SCC#N)C(=O)CC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL2113619\n",
      "N#Cc1ccc(C2CCCc3cncn32)cc1\tCHEMBL9298\n",
      "N#Cc1ccc(Cn2ccnc2)c2c(=O)c3ccccc3oc12\tCHEMBL442373\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "c1cn(CCC2CCCc3occc32)cn1\tCHEMBL55144\n",
      "c1cn(CCC2CCCc3sccc32)cn1\tCHEMBL291700\n",
      "N#Cc1ccc(Cn2ccnc2)cc1\tCHEMBL349822\n",
      "N#Cc1ccc(Cn2ccnc2)c2oc3ccccc3c(=O)c12\tCHEMBL421149\n",
      "C1=C(CCn2ccnc2)c2cccnc2CC1\tCHEMBL293122\n",
      "c1cc2cc(-n3ccnc3)ccc2cn1\tCHEMBL299066\n",
      "C[C@]12CCC3C(CCC4=C(SCF)C(=O)CC[C@@]43C)C1CCC2=O\tCHEMBL41517\n",
      "C[C@]12CC=C3C(CCC4=C(S)C(=O)CC[C@]34C)C1CCC2=O\tCHEMBL41033\n",
      "C(\\Cn1ccnc1)=C1\\CCCc2sccc21\tCHEMBL57513\n",
      "c1cnc2c(c1)CCCC2n1ccnc1\tCHEMBL301608\n",
      "O=c1c2ccccc2oc2cc(Cn3ccnc3)ccc12\tCHEMBL350922\n",
      "Brc1ccc(C2CCCc3cncn32)cc1.Cl\tCHEMBL1203766\n",
      "c1cc(CCn2ccnc2)c2cccnc2c1\tCHEMBL54803\n",
      "O=c1c2ccccc2oc2c([N+](=O)[O-])ccc(Cn3ccnc3)c12\tCHEMBL307160\n",
      "c1ccc2c(c1)CCCC2CCn1ccnc1\tCHEMBL53644\n",
      "O=C(O)c1ccc(C2CCCc3cncn32)cc1\tCHEMBL163413\n",
      "CCOC(=O)c1ccc(C2CCCc3cncn32)cc1.Cl\tCHEMBL1203760\n",
      "C[C@]12CC=C3C(CCC4=C(SCCl)C(=O)CC[C@]34C)C1CCC2=O\tCHEMBL442542\n",
      "COc1ccc2c(=O)c(Cn3ccnc3)c(-c3ccccc3)oc2c1\tCHEMBL158635\n",
      "Cc1cncn1Cc1ccc(C#N)cc1\tCHEMBL162496\n",
      "O=c1ccoc2c(Cn3ccnc3)ccc(Br)c12\tCHEMBL161530\n",
      "COCOC1=C2CCC3C4CCC(=O)[C@@]4(C)CCC3[C@@]2(C)CCC1=O\tCHEMBL290309\n",
      "c1cnc2c(c1)C(CCn1ccnc1)CCC2\tCHEMBL292899\n",
      "c1cc2ccc(Cn3ccnc3)cc2cn1\tCHEMBL298720\n",
      "CSc1nc2ccccn2c(=N)c1S(=O)(=O)c1ccccc1\tCHEMBL355905\n",
      "Cc1ccc(C2CCCc3cncn32)cc1.Cl\tCHEMBL1203757\n",
      "Cl.N#Cc1ccc(C2CCCc3cncn32)cc1\tCHEMBL468419\n",
      "Cl.N#Cc1ccc(C2CCCc3cncn32)cc1\tCHEMBL468419\n",
      "C1=C(CCCn2ccnc2)c2cccnc2CC1\tCHEMBL53290\n",
      "C[C@]12CCC3C(CCC4=C(SCCl)C(=O)CC[C@@]43C)C1CCC2=O\tCHEMBL40432\n",
      "O=c1c2ccccc2oc2c(Br)ccc(Cn3ccnc3)c12\tCHEMBL78322\n",
      "C(\\Cn1ccnc1)=C1/CCCc2occc21\tCHEMBL56059\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=C(SC(F)F)C(=O)CC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL2113524\n",
      "COCSC1=C2CCC3C4CCC(=O)[C@@]4(C)CCC3[C@@]2(C)CCC1=O\tCHEMBL291279\n",
      "C(\\Cn1ccnc1)=C1\\CCCc2occc21\tCHEMBL55083\n",
      "N#Cc1ccc(Cn2ccnc2)c2occc(=O)c12\tCHEMBL161230\n",
      "c1cnc2c(c1)C(n1ccnc1)CCC2\tCHEMBL52509\n",
      "C(\\Cn1ccnc1)=C1/CCCc2sccc21\tCHEMBL55558\n",
      "N#Cc1ccc(C2CCCc3cncn32)cc1\tCHEMBL9298\n",
      "Cl.N#Cc1ccc(C2CCc3cncn32)cc1\tCHEMBL1203762\n",
      "CSC1=C2CCC3C4CCC(=O)[C@@]4(C)CCC3[C@@]2(C)CCC1=O\tCHEMBL39248\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=C(O)C(=O)CC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL132530\n",
      "O=c1c2ccccc2oc2c(Cn3ccnc3)ccc(Br)c12\tCHEMBL421829\n",
      "c1cnc2c(c1)C(Cn1ccnc1)CCC2\tCHEMBL300109\n",
      "N#Cc1ccc(C2CCCCc3cncn32)cc1\tCHEMBL162834\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "C[C@]12CC=C3C(CCC4=C(O)C(=O)CC[C@]34C)C1CCC2=O\tCHEMBL288190\n",
      "CSCSC1=C2CCC3C4CCC(=O)[C@@]4(C)CCC3[C@@]2(C)CCC1=O\tCHEMBL39434\n",
      "C1=C(Cn2ccnc2)c2cccnc2CC1\tCHEMBL53068\n",
      "O=c1ccoc2cc(Cn3ccnc3)ccc12\tCHEMBL158984\n",
      "O=c1ccoc2cc(Cn3cnnc3)ccc12\tCHEMBL157925\n",
      "OCc1ccc(C2CCCc3cncn32)cc1\tCHEMBL348913\n",
      "N#Cc1ccc(C2CCCc3cncn32)cc1\tCHEMBL9298\n",
      "CCCCCCCCC[C@H]1C[C@](CC)(c2ccncc2)C(=O)NC1=O\tCHEMBL123494\n",
      "Nc1ccc(C2(C3CCCCC3)CCC(=O)NC2=O)cc1\tCHEMBL70901\n",
      "CC[C@]1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL2051951\n",
      "CC[C@]1(c2ccncc2)C[C@H](C)C(=O)NC1=O\tCHEMBL124230\n",
      "COc1ccc2c(c1)OC(c1ccccc1)CC2=O\tCHEMBL9687\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "N#Cc1ccc(C2CCCc3cncn32)cc1\tCHEMBL9298\n",
      "CN1C(=O)CC(c2ccc(N)cc2)C1=O\tCHEMBL357775\n",
      "CN1C(=O)CC(c2ccc(N)cc2)C1=O\tCHEMBL357775\n",
      "CCCCCCCC[C@H]1C[C@](CCCCCCCC)(c2ccncc2)C(=O)NC1=O\tCHEMBL338746\n",
      "CCCCCCCC[C@]1(c2ccncc2)CCC(=O)NC1=O\tCHEMBL88412\n",
      "CCCC[C@H]1C[C@](CC)(c2ccncc2)C(=O)NC1=O\tCHEMBL333185\n",
      "O=C1/C(=C\\c2ccncc2)C(c2ccc(Cl)cc2)Cn2cccc21\tCHEMBL9165\n",
      "Nc1ccc(C2(C3CCCC3)CCC(=O)NC2=O)cc1\tCHEMBL305483\n",
      "O=C1/C(=C\\c2ccncc2)C(c2ccccc2)c2ccccc21\tCHEMBL9179\n",
      "COc1ccc2c(c1)OC(c1ccccc1)CC2n1ccnc1\tCHEMBL95109\n",
      "CC[C@]1(c2ccncc2)C[C@@H](C)C(=O)NC1=O\tCHEMBL124118\n",
      "CC[C@]1(c2ccncc2)CC(C)(C)C(=O)NC1=O\tCHEMBL269761\n",
      "CCCCCCCC[C@H]1C[C@](CC)(c2ccncc2)C(=O)NC1=O\tCHEMBL331522\n",
      "CC[C@]1(c2ccncc2)CCC(=O)NC1=O\tCHEMBL330452\n",
      "CC[C@@]1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL1394757\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "CCC1(c2ccc(N)cc2)CC(=O)NC1=O\tCHEMBL151212\n",
      "CCC1(c2ccc(N)cc2)CC(=O)NC1=O\tCHEMBL151212\n",
      "COc1ccc2c(c1)C(N)=C(CC1=CCNC=C1)C2=O\tCHEMBL1096024\n",
      "CCCCCCCC[C@H]1C[C@](CC)(c2ccncc2)C(=O)NC1=O\tCHEMBL331522\n",
      "O=C1CC(c2ccccc2)Oc2cc(O)ccc21\tCHEMBL97542\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "CN1C(=O)CC(C)(c2ccc(N)cc2)C1=O\tCHEMBL151691\n",
      "CN1C(=O)CC(C)(c2ccc(N)cc2)C1=O\tCHEMBL151691\n",
      "CCCCCCCC[C@H]1C[C@@](CC)(c2ccncc2)C(=O)NC1=O\tCHEMBL2111801\n",
      "Nc1ccc(C2(C3CCCCC3)CCC(=O)NC2=O)cc1\tCHEMBL70901\n",
      "Nc1ccc(C2(C3CCCCC3)CCC(=O)NC2=O)cc1\tCHEMBL70901\n",
      "CC1(c2ccc(N)cc2)CC(=O)NC1=O\tCHEMBL149238\n",
      "CC1(c2ccc(N)cc2)CC(=O)NC1=O\tCHEMBL149238\n",
      "Nc1ccc(C2CC(=O)NC2=O)cc1\tCHEMBL148903\n",
      "Nc1ccc(C2CC(=O)NC2=O)cc1\tCHEMBL148903\n",
      "Oc1ccc2c(c1)OC(c1ccccc1)CC2n1ccnc1\tCHEMBL95919\n",
      "O=C(OCc1cccnc1)C12CC3CC(CC(C3)C1)C2\tCHEMBL113647\n",
      "C[C@H](OC(=O)C12CC3CC(CC(C3)C1)C2)c1ccncc1\tCHEMBL110524\n",
      "CC(C)(OC(=O)C12CC3CC(CC(C3)C1)C2)c1ccncc1\tCHEMBL112908\n",
      "C[C@@H](OC(=O)C12CC3CC(CC(C3)C1)C2)c1ccncc1\tCHEMBL112292\n",
      "C[C@H](OC(=O)C12CC3CC(CC(C3)C1)C2)c1cccnc1\tCHEMBL112509\n",
      "CC(=O)N1CCN(c2ccc(OCC3COC(Cn4ccnc4)(c4ccc(Cl)cc4Cl)O3)cc2)CC1\tCHEMBL157101\n",
      "C[C@H](NC(=O)/C=C/c1ccccc1)c1cccc(N2CCOCC2)c1\tCHEMBL317935\n",
      "O=C(NCc1ccncc1)C12CC3CC(CC(C3)C1)C2\tCHEMBL114447\n",
      "O=C(OCc1ccncc1)C12CC3CC(CC(C3)C1)C2\tCHEMBL113242\n",
      "C[C@@H](OC(=O)C12CC3CC(CC(C3)C1)C2)c1cccnc1\tCHEMBL325461\n",
      "O=C(NCc1cccnc1)C12CC3CC(CC(C3)C1)C2\tCHEMBL109347\n",
      "CC(C)(OC(=O)C12CC3CC(CC(C3)C1)C2)c1cccnc1\tCHEMBL431843\n",
      "CC12CCC3C4CCC(=O)C=C4CCC3C1CCC2=O\tCHEMBL309113\n",
      "O=C(Cc1ccncc1)Oc1ccccc1\tCHEMBL323546\n",
      "CC(C(=O)OC1CCCCC1)c1ccncc1\tCHEMBL102981\n",
      "CCCCCC[C@]1(c2ccncc2)CCC(=O)NC1=O\tCHEMBL441404\n",
      "C[C@]1(c2ccncc2)CCC(=O)NC1=O\tCHEMBL89046\n",
      "CCCCCCC[C@]1(c2ccncc2)CCC(=O)NC1=O\tCHEMBL313485\n",
      "C#C[C@@H]1CC2C3CCC(=O)C3(C)CCC2C2(C)CCC(=O)C=C12\tCHEMBL25700\n",
      "CC[C@H]1CC2C3CCC(=O)C3(C)CCC2C2(C)CCC(=O)C=C12\tCHEMBL24021\n",
      "CC1CCC(C(C)C)C(OC(=O)Cc2ccncc2)C1\tCHEMBL105298\n",
      "CC12CCC3C(C[C@@H](Cc4ccccc4)C4=CC(=O)CCC43C)C1CCC2=O\tCHEMBL24897\n",
      "C=C[C@@H]1CC2C3CCC(=O)C3(C)CCC2C2(C)CCC(=O)C=C12\tCHEMBL24410\n",
      "CC1CCC(OC(=O)Cc2ccncc2)CC1\tCHEMBL321729\n",
      "CC(C)CC(C)OC(=O)Cc1ccncc1\tCHEMBL104126\n",
      "CCCN1C(=O)CC[C@](CC)(c2ccncc2)C1=O\tCHEMBL314351\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=CC(=O)CC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL274826\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=CCCC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL1629804\n",
      "O=C(Cc1ccncc1)OC12CC3CC(CC(C3)C1)C2\tCHEMBL104047\n",
      "CCCCCN1C(=O)CC[C@](CC)(c2ccncc2)C1=O\tCHEMBL312960\n",
      "CC12CCC3C(C(=O)C=C4CCCCC43CO)C1CCC2=O\tCHEMBL302741\n",
      "CC1(OC(=O)Cc2ccncc2)CCCCC1\tCHEMBL105044\n",
      "CC1(C)C2CCC1(C)C(OC(=O)Cc1ccncc1)C2\tCHEMBL102536\n",
      "CC12CCC3C(CCC4=C(Oc5ccccc5)C(=O)CCC43C)C1CCC2=O\tCHEMBL334391\n",
      "C[C@]12CC[C@H]3[C@@H](CC=C4CCCC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL2311169\n",
      "CC12CCC=CC1=CCC1C2CCC2(C)C1CC[C@@H]2O\tCHEMBL71117\n",
      "CC12CCC3C(CC=C4CCCCC43CO)C1CCC2O\tCHEMBL308829\n",
      "CC[C@]1(c2ccncc2)CCC(=O)NC1=O\tCHEMBL330452\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "CCCCCCCCN1C(=O)CCC(CC)(c2ccncc2)C1=O\tCHEMBL39661\n",
      "CCCC[C@H]1CC2C3CCC(=O)C3(C)CCC2C2(C)CCC(=O)C=C12\tCHEMBL24946\n",
      "CC1CCC(OC(=O)Cc2ccncc2)CC1\tCHEMBL321729\n",
      "CC1CCC(C(C)C)C(OC(=O)Cc2ccncc2)C1\tCHEMBL105298\n",
      "CC[C@]1(c2ccncc2)CCC(=O)N(C)C1=O\tCHEMBL314991\n",
      "CC1CCC(OC(=O)Cc2ccncc2)C1\tCHEMBL104566\n",
      "CCCCC[C@]1(c2ccncc2)CCC(=O)NC1=O\tCHEMBL89102\n",
      "CC12CCCCC1=CC(=O)C1C2CCC2(C)C(O)CCC12\tCHEMBL302249\n",
      "CC12CCC3C(CC=C4CCCCC43C=O)C1CCC2=O\tCHEMBL71736\n",
      "CCC[C@H]1CC2C3CCC(=O)C3(C)CCC2C2(C)CCC(=O)C=C12\tCHEMBL25695\n",
      "CC12CCC3C(CCC4=C(Sc5ccccc5)C(=O)CCC43C)C1CCC2=O\tCHEMBL336387\n",
      "CC(C)(C)C1CCC(OC(=O)Cc2ccncc2)CC1\tCHEMBL102884\n",
      "CCCCCCCCC[C@]1(c2ccncc2)CCC(=O)NC1=O\tCHEMBL91363\n",
      "CC12CCC3C(CC=C4C=CCCC43C)C1CCC2=O\tCHEMBL71152\n",
      "CC(C)(C(=O)OC1CCCCC1)c1ccncc1\tCHEMBL103685\n",
      "CC12CCC3C4CCC=CC4=CC(=O)C3C1CCC2=O\tCHEMBL71612\n",
      "O=C(Cc1ccncc1)OC1CCCCC1\tCHEMBL102409\n",
      "CC12CCC3C(C(=O)C=C4C=CCCC43C)C1CCC2=O\tCHEMBL262723\n",
      "CC12CCC3C(C(=O)C=C4C=CCCC43CO)C1CCC2=O\tCHEMBL302530\n",
      "CC12CCC3C(C[C@@H](c4ccccc4)C4=CC(=O)CCC43C)C1CCC2=O\tCHEMBL281871\n",
      "CCCCCCCN1C(=O)CC[C@](CC)(c2ccncc2)C1=O\tCHEMBL314125\n",
      "O=C(Cc1ccncc1)OC1=CC2CCC1C2\tCHEMBL103040\n",
      "CCC[C@@H]1CC2C3CCC(=O)C3(C)CCC2C2(C)CCC(=O)C=C12\tCHEMBL282673\n",
      "CCCC[C@@H]1CC2C3CCC(=O)C3(C)CCC2C2(C)CCC(=O)C=C12\tCHEMBL24037\n",
      "C[C@H]1CC2C3CCC(=O)C3(C)CCC2C2(C)CCC(=O)C=C12\tCHEMBL25588\n",
      "CC12CCC3C(CC=C4CCCCC43C)C1C[C@@H](Br)C2=O\tCHEMBL71363\n",
      "CCCCCCCC[C@]1(c2ccncc2)CCC(=O)NC1=O\tCHEMBL88412\n",
      "CCC1(c2ccncc2)CCC(=O)NC1=O\tCHEMBL289116\n",
      "CC12CCC3C(C[C@H](c4ccccc4)C4=CC(=O)CCC43C)C1CCC2=O\tCHEMBL24580\n",
      "CCCCCCCCCN1C(=O)CC[C@](CC)(c2ccncc2)C1=O\tCHEMBL88421\n",
      "CC12CCC3C(C(=O)C=C4CCCCC43C=O)C1CCC2=O\tCHEMBL71169\n",
      "CC12CCC=CC1=CC(=O)C1C2CCC2(C)C1CC[C@@H]2O\tCHEMBL311400\n",
      "CC[C@@H]1CC2C3CCC(=O)C3(C)CCC2C2(C)CCC(=O)C=C12\tCHEMBL25059\n",
      "CCC1CCC(OC(=O)Cc2ccncc2)CC1\tCHEMBL101743\n",
      "CCCC[C@]1(c2ccncc2)CCC(=O)NC1=O\tCHEMBL88842\n",
      "CC1C(OC(=O)Cc2ccncc2)CC2CC1C2(C)C\tCHEMBL319026\n",
      "CC(C)[C@@H]1CC2C3CCC(=O)C3(C)CCC2C2(C)CCC(=O)C=C12\tCHEMBL24387\n",
      "CC1C(OC(=O)Cc2ccncc2)CC2CC1C2(C)C\tCHEMBL319026\n",
      "CC12CCC3C(C(=O)C=C4CCCCC43CO)C1CCC2O\tCHEMBL421491\n",
      "CC12CCC3C4CCCCC4=CC(=O)C3C1CCC2=O\tCHEMBL71082\n",
      "CC(C)CCOC(=O)Cc1ccncc1\tCHEMBL323585\n",
      "CCCCCCN1C(=O)CC[C@](CC)(c2ccncc2)C1=O\tCHEMBL316262\n",
      "CCCCCCCCCCN1C(=O)CC[C@](CC)(c2ccncc2)C1=O\tCHEMBL90856\n",
      "CC1(C)C2CCC1(C)C(OC(=O)Cc1ccncc1)C2\tCHEMBL102536\n",
      "CC12CCCCC1=CCC1C2CCC2(C)C(O)CCC12\tCHEMBL68674\n",
      "CC12CCC3C(C1CCC2=O)[C@H](Sc1ccccc1)CC1=C(Oc2ccccc2)C(=O)CCC13C\tCHEMBL335408\n",
      "CC(C)[C@H]1CC2C3CCC(=O)C3(C)CCC2C2(C)CCC(=O)C=C12\tCHEMBL25473\n",
      "C[C@]12CC[C@H]3[C@@H](CC=C4CCCC[C@@]43CO)[C@@H]1CCC2=O\tCHEMBL1630911\n",
      "C[C@@H]1CC2C3CCC(=O)C3(C)CCC2C2(C)CCC(=O)C=C12\tCHEMBL284238\n",
      "C[C@]12CC[C@H]3[C@H]([C@@H]1CCC2=O)[C@H](Sc1ccccc1)CC1=CC(=O)CC[C@@]13C\tCHEMBL3245349\n",
      "CCCCN1C(=O)CC[C@](CC)(c2ccncc2)C1=O\tCHEMBL328546\n",
      "O=C(Cc1ccncc1)OC1CCCC1\tCHEMBL105289\n",
      "CCC[C@]1(c2ccncc2)CCC(=O)NC1=O\tCHEMBL444844\n",
      "CCCCCCCCN1C(=O)CC[C@](CC)(c2ccncc2)C1=O\tCHEMBL88918\n",
      "CC(C)OC(=O)Cc1ccncc1\tCHEMBL102510\n",
      "CC12CCC3C(C(=O)C=C4CCCCC43C)C1CCC2=O\tCHEMBL70367\n",
      "CC12CCC3C(C(=O)C=C4C=CCCC43C=O)C1CCC2=O\tCHEMBL302305\n",
      "CC12CCC3C(C[C@H](Cc4ccccc4)C4=CC(=O)CCC43C)C1CCC2=O\tCHEMBL25523\n",
      "O=C(Cc1ccncc1)OC1=CC2CCC1C2\tCHEMBL103040\n",
      "CCN1C(=O)CC[C@](CC)(c2ccncc2)C1=O\tCHEMBL327239\n",
      "CC(=O)N1CCN(c2ccc(OCC3COC(Cn4ccnc4)(c4ccc(Cl)cc4Cl)O3)cc2)CC1\tCHEMBL157101\n",
      "CC(=O)N1CCN(c2ccc(OC[C@@H]3CO[C@](Cn4ccnc4)(c4ccc(Cl)cc4Cl)O3)cc2)CC1\tCHEMBL328863\n",
      "CC(=O)N1CCN(c2ccc(OC[C@H]3CO[C@@](Cn4ccnc4)(c4ccc(Cl)cc4Cl)O3)cc2)CC1\tCHEMBL319160\n",
      "CC(=O)N1CCN(c2ccc(OCC3COC(Cn4ccnc4)(c4ccc(Cl)cc4Cl)O3)cc2)CC1\tCHEMBL157101\n",
      "Nc1ccc(C2CCC(=O)NC2=O)cc1\tCHEMBL28880\n",
      "CCCC(C)C1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL29521\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "CCCCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL29863\n",
      "CCCCCCCCN1C(=O)CCC(CC)(c2ccncc2)C1=O\tCHEMBL39661\n",
      "CCCCCCCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL29338\n",
      "CCCCCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL25735\n",
      "CCC(C)CC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL287647\n",
      "CC(C)C1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL282573\n",
      "CC(C)CC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL28669\n",
      "CC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL29138\n",
      "CCC(C)C1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL28668\n",
      "CCCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL29864\n",
      "CC(C)CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL29230\n",
      "CCCCCCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL29339\n",
      "C(=C1\\CCc2ccccc21)\\c1c[nH]cn1\tCHEMBL177468\n",
      "C(=C1/CCc2ccccc21)\\c1c[nH]cn1\tCHEMBL178036\n",
      "N#Cc1ccc2c(c1)/C(=C/c1c[nH]cn1)CCC2\tCHEMBL177413\n",
      "N#Cc1ccc2c(c1)/C(=C\\c1c[nH]cn1)CCC2\tCHEMBL177274\n",
      "N#Cc1ccc2c(c1)CCC/C2=C/c1c[nH]cn1\tCHEMBL367483\n",
      "N#Cc1ccc2c(c1)CC/C2=C\\c1c[nH]cn1\tCHEMBL177921\n",
      "N#Cc1ccc2c(c1)CC/C2=C/c1ncc[nH]1\tCHEMBL175091\n",
      "Clc1ccc2c(c1)/C(=C/c1c[nH]cn1)CCC2\tCHEMBL175144\n",
      "Fc1ccc2c(c1)CC/C2=C\\c1c[nH]cn1\tCHEMBL359817\n",
      "C(=C1\\CCCc2ccccc21)\\c1c[nH]cn1\tCHEMBL362187\n",
      "C(=C1/CCCc2ccccc21)\\c1c[nH]cn1\tCHEMBL367719\n",
      "Fc1ccc2c(c1)CC/C2=C/c1c[nH]cn1\tCHEMBL179247\n",
      "Clc1ccc2c(c1)CC/C2=C\\c1c[nH]cn1\tCHEMBL177692\n",
      "Clc1ccc2c(c1)CC/C2=C/c1c[nH]cn1\tCHEMBL175688\n",
      "Brc1ccc2c(c1)CC/C2=C\\c1c[nH]cn1\tCHEMBL177293\n",
      "Brc1ccc2c(c1)CC/C2=C/c1c[nH]cn1\tCHEMBL174579\n",
      "N#Cc1ccc(C2CCCc3cncn32)cc1\tCHEMBL9298\n",
      "Brc1ccc2c(c1)CC/C2=C/c1cccnc1\tCHEMBL176118\n",
      "COc1ccc2c(c1)CC/C2=C\\c1cccnc1\tCHEMBL176609\n",
      "COc1ccc2c(c1)CC/C2=C/c1cccnc1\tCHEMBL176101\n",
      "COc1ccc2c(c1)CCC/C2=C\\c1cccnc1\tCHEMBL362441\n",
      "COc1ccc2c(c1)CCC/C2=C/c1cccnc1\tCHEMBL176098\n",
      "COc1ccc2c(c1)/C(=C/c1cccnc1)CC2\tCHEMBL366772\n",
      "COc1cc2c(cc1OC)/C(=C/c1cccnc1)CCC2\tCHEMBL366669\n",
      "CCOc1ccc2c(c1)CC/C2=C\\c1cccnc1\tCHEMBL425323\n",
      "CCOc1ccc2c(c1)CC/C2=C/c1cccnc1\tCHEMBL176886\n",
      "C(=C1\\CCc2cc(OCc3ccccc3)ccc21)\\c1cccnc1\tCHEMBL177268\n",
      "Cc1ccc2c(c1)/C(=C/c1cccnc1)CC2\tCHEMBL175550\n",
      "Cc1ccc2c(c1)/C(=C\\c1cccnc1)CC2\tCHEMBL366570\n",
      "Cc1cccc2c1CC/C2=C\\c1cccnc1\tCHEMBL368305\n",
      "Cc1cccc2c1CC/C2=C/c1cccnc1\tCHEMBL176009\n",
      "Fc1cccc2c1CC/C2=C\\c1cccnc1\tCHEMBL366490\n",
      "Fc1cccc2c1CC/C2=C/c1cccnc1\tCHEMBL176076\n",
      "Clc1cccc2c1CC/C2=C\\c1cccnc1\tCHEMBL176287\n",
      "Clc1cccc2c1CC/C2=C/c1cccnc1\tCHEMBL369623\n",
      "COc1cccc2c1/C(=C/c1cccnc1)CC2\tCHEMBL175791\n",
      "N#Cc1ccc(C2CCCc3cncn32)cc1\tCHEMBL9298\n",
      "C(=C1\\CCc2ccccc21)\\c1ccncc1\tCHEMBL175962\n",
      "C(=C1/CCc2ccccc21)\\c1ccncc1\tCHEMBL179149\n",
      "C(=C1\\CCCc2ccccc21)\\c1ccncc1\tCHEMBL360026\n",
      "Fc1ccc2c(c1)CC/C2=C\\c1ccncc1\tCHEMBL424960\n",
      "Fc1ccc2c(c1)CC/C2=C/c1ccncc1\tCHEMBL179144\n",
      "Clc1ccc2c(c1)CC/C2=C\\c1ccncc1\tCHEMBL175554\n",
      "Clc1ccc2c(c1)CC/C2=C/c1ccncc1\tCHEMBL175799\n",
      "Brc1ccc2c(c1)CC/C2=C\\c1ccncc1\tCHEMBL366773\n",
      "Brc1ccc2c(c1)CC/C2=C/c1ccncc1\tCHEMBL179222\n",
      "COc1ccc2c(c1)CC/C2=C\\c1ccncc1\tCHEMBL359942\n",
      "COc1ccc2c(c1)CC/C2=C/c1ccncc1\tCHEMBL175609\n",
      "COc1ccc2c(c1)CCC/C2=C\\c1ccncc1\tCHEMBL175979\n",
      "COc1ccc2c(c1)OC(c1ccccc1)CC2n1cncn1\tCHEMBL185165\n",
      "COc1ccc2c(c1)OC(c1ccccc1)CC2n1cnnc1\tCHEMBL184952\n",
      "Oc1ccc2c(c1)OC(c1ccccc1)CC2n1cncn1\tCHEMBL187185\n",
      "COc1ccc2c(c1)OC(c1ccccc1)CC2n1ccnc1\tCHEMBL95109\n",
      "Oc1ccc2c(c1)OC(c1ccccc1)CC2n1ccnc1\tCHEMBL95919\n",
      "C=CCSc1oc2cc(OCc3ccccc3)ccc2c(=O)c1-c1ccc(OC)cc1\tCHEMBL185966\n",
      "COc1ccc(-c2c(SCc3ccccc3)oc3cc(OCc4ccccc4)ccc3c2=O)cc1\tCHEMBL361872\n",
      "C=CCSc1oc2cc(OC)ccc2c(=O)c1-c1ccccc1\tCHEMBL264724\n",
      "COc1ccc2c(=O)c(-c3ccccc3)c(SCc3ccccc3)oc2c1\tCHEMBL189170\n",
      "COc1ccc2c(=O)c(-c3ccccc3)c(SCc3ccncc3)oc2c1\tCHEMBL185809\n",
      "COc1ccc2c(=O)c(-c3ccccc3)c(SCc3cccnc3)oc2c1\tCHEMBL185819\n",
      "COc1ccc2c(=O)c(-c3ccccc3)c(SCc3ccccn3)oc2c1\tCHEMBL362906\n",
      "COc1ccc2c(=O)c(-c3ccc(C)cc3)c(SCc3ccncc3)oc2c1\tCHEMBL184051\n",
      "COc1ccc(-c2c(SCc3ccncc3)oc3cc(OC)ccc3c2=O)cc1\tCHEMBL361653\n",
      "O=c1c(-c2ccccc2)c(SCc2ccncc2)oc2cc(OCc3ccccc3)ccc12\tCHEMBL188664\n",
      "COc1ccc(-c2c(SCc3ccncc3)oc3cc(OCc4ccccc4)ccc3c2=O)cc1\tCHEMBL186079\n",
      "O=c1c(-c2ccccc2)c(SCc2ccncc2)oc2cc(O)ccc12\tCHEMBL184749\n",
      "O=c1c(-c2ccccc2)c(SCc2cccnc2)oc2cc(O)ccc12\tCHEMBL364326\n",
      "O=c1c(-c2ccc(O)cc2)c(SCc2ccncc2)oc2cc(O)ccc12\tCHEMBL185950\n",
      "COc1ccc(-c2c(SCc3ccncc3)oc3cc(O)ccc3c2=O)cc1\tCHEMBL187068\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "COc1ccc(-c2coc3cc(O)cc(O)c3c2=O)cc1\tCHEMBL131921\n",
      "COc1ccc2c(=O)c(-c3ccccc3)c(-n3ccnc3)oc2c1\tCHEMBL192306\n",
      "COc1ccc2c(=O)c(-c3ccccc3)c(-n3ccnc3)oc2c1\tCHEMBL192306\n",
      "COc1ccc(-c2c(-n3ccnc3)oc3cc(OC)ccc3c2=O)cc1\tCHEMBL189472\n",
      "COc1ccc(-c2c(-n3ccnc3)oc3cc(OC)ccc3c2=O)cc1\tCHEMBL189472\n",
      "O=c1c(-c2ccccc2)c(-n2ccnc2)oc2cc(OCc3ccccc3)ccc12\tCHEMBL363493\n",
      "O=c1c(-c2ccccc2)c(-n2ccnc2)oc2cc(OCc3ccccc3)ccc12\tCHEMBL363493\n",
      "COc1ccc(-c2c(-n3ccnc3)oc3cc(OCc4ccccc4)ccc3c2=O)cc1\tCHEMBL189274\n",
      "COc1ccc(-c2c(-n3ccnc3)oc3cc(OCc4ccccc4)ccc3c2=O)cc1\tCHEMBL189274\n",
      "COc1ccc2c(=O)c(-c3ccccc3)c(-n3cncn3)oc2c1\tCHEMBL190099\n",
      "COc1ccc2c(=O)c(-c3ccccc3)c(-n3cncn3)oc2c1\tCHEMBL190099\n",
      "COc1ccc2c(=O)c(-c3ccccc3)c(Sc3ncc[nH]3)oc2c1\tCHEMBL191488\n",
      "COc1ccc2c(=O)c(-c3ccccc3)c(Sc3nccn3C)oc2c1\tCHEMBL190848\n",
      "COc1ccc2c(=O)c(-c3ccccc3)c(Sc3nnc[nH]3)oc2c1\tCHEMBL190208\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "COc1ccc(-c2coc3cc(O)cc(O)c3c2=O)cc1\tCHEMBL131921\n",
      "COc1ccc(-c2coc3cc(O)cc(O)c3c2=O)cc1\tCHEMBL131921\n",
      "C(=C1/CCc2ccccc21)\\c1cccnc1\tCHEMBL363621\n",
      "C(=C1\\CCCc2ccccc21)\\c1cccnc1\tCHEMBL193606\n",
      "C(=C1/CCCc2ccccc21)\\c1cccnc1\tCHEMBL365120\n",
      "Fc1ccc2c(c1)CC/C2=C\\c1cccnc1\tCHEMBL193997\n",
      "Fc1ccc2c(c1)CC/C2=C/c1cccnc1\tCHEMBL383806\n",
      "Clc1ccc2c(c1)CC/C2=C\\c1cccnc1\tCHEMBL383807\n",
      "Clc1ccc2c(c1)CC/C2=C/c1cccnc1\tCHEMBL193652\n",
      "Brc1ccc2c(c1)CC/C2=C\\c1cccnc1\tCHEMBL194440\n",
      "COc1ccc2c(c1)CCC/C2=C/c1ccncc1\tCHEMBL193496\n",
      "COc1ccc2c(c1)/C(=C/c1ccncc1)CC2\tCHEMBL365314\n",
      "COc1ccc2c(c1)/C(=C\\c1ccncc1)CC2\tCHEMBL370281\n",
      "COc1cc2c(cc1OC)/C(=C/c1ccncc1)CCC2\tCHEMBL193858\n",
      "Cc1ccc2c(c1)/C(=C/c1ccncc1)CC2\tCHEMBL371742\n",
      "Cc1ccc2c(c1)/C(=C\\c1ccncc1)CC2\tCHEMBL192077\n",
      "COc1ccc2c(c1)CC/C2=C\\c1cncs1\tCHEMBL192155\n",
      "COc1ccc2c(c1)CC/C2=C/c1cncs1\tCHEMBL195354\n",
      "Fc1ccc2c(c1)CC/C2=C\\c1cncnc1\tCHEMBL195248\n",
      "Fc1ccc2c(c1)CC/C2=C/c1cncnc1\tCHEMBL364956\n",
      "Fc1ccc2c(c1)CC/C2=C\\c1cccc2ncccc12\tCHEMBL195580\n",
      "Fc1ccc2c(c1)CC/C2=C/c1cccc2ncccc12\tCHEMBL195653\n",
      "Fc1ccc2c(c1)CC/C2=C\\c1cccc2cnccc12\tCHEMBL370530\n",
      "Fc1ccc2c(c1)CC/C2=C/c1cccc2cnccc12\tCHEMBL363849\n",
      "Fc1ccc2c(c1)CC/C2=C\\c1ccnc2ccccc12\tCHEMBL371509\n",
      "Fc1ccc2c(c1)CC/C2=C/c1ccnc2ccccc12\tCHEMBL371275\n",
      "C(=C1c2ccccc2-c2ccccc21)c1cccnc1\tCHEMBL194928\n",
      "C(=C(c1ccccc1)c1ccccc1)c1cccnc1\tCHEMBL370317\n",
      "C(=C1c2ccccc2-c2ccccc21)c1ccncc1\tCHEMBL383145\n",
      "C(=C(c1ccccc1)c1ccccc1)c1ccncc1\tCHEMBL381194\n",
      "CC1C/C(=C\\c2cccnc2)c2ccccc21\tCHEMBL195688\n",
      "CC1C/C(=C/c2cccnc2)c2ccccc21\tCHEMBL373074\n",
      "C(=C1\\CC(c2ccccc2)c2ccccc21)\\c1cccnc1\tCHEMBL193555\n",
      "C(=C1\\CCc2ccccc21)\\c1cccnc1\tCHEMBL364080\n",
      "c1cncc(-c2ccc3ccccc3c2)c1\tCHEMBL365735\n",
      "COc1ccc2cc(-c3cccnc3)ccc2c1\tCHEMBL362968\n",
      "Oc1ccc2cc(-c3cccnc3)ccc2c1\tCHEMBL195571\n",
      "Brc1ccc2cc(-c3cccnc3)ccc2c1\tCHEMBL193534\n",
      "CCOc1ccc2cc(-c3cccnc3)ccc2c1\tCHEMBL193343\n",
      "N#Cc1ccc2cc(-c3cccnc3)ccc2c1\tCHEMBL196796\n",
      "COc1ccc2cc(-c3cccnc3)ccc2c1Cl\tCHEMBL197576\n",
      "COc1ccc2cc(-c3cccnc3)ccc2c1Br\tCHEMBL196328\n",
      "COc1ccc2c(Cl)c(-c3cccnc3)ccc2c1Cl\tCHEMBL381014\n",
      "COc1ccc2ccc(-c3cccnc3)c(Cl)c2c1\tCHEMBL371430\n",
      "COC(=O)c1ccc2cc(-c3cccnc3)ccc2c1\tCHEMBL438802\n",
      "c1ccc2cc(-n3ccnc3)ccc2c1\tCHEMBL195996\n",
      "COc1cc2ccccc2cc1-n1ccnc1\tCHEMBL194472\n",
      "N#Cc1ccc(C2CCCc3cncn32)cc1\tCHEMBL9298\n",
      "C[C@]12CCC3C(CC[C@H]4C=CCC[C@]34C)C1CCC2=O\tCHEMBL194641\n",
      "C[C@]12CC[C@H]3[C@@H](CC[C@H]4[C@@H]5O[C@@H]5CC[C@]34C)[C@@H]1CCC2=O\tCHEMBL1629802\n",
      "C[C@]12CC[C@H]3[C@@H](CC[C@H]4C(=O)[C@H](O)CC[C@]34C)[C@@H]1CCC2=O\tCHEMBL1630280\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=C(O)C(=O)CC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL132530\n",
      "C[C@]12CCC3C(CCC4=C(O)C(=O)CC[C@@]43C)C1CCC(=O)O2\tCHEMBL197451\n",
      "CC(C)(C#N)c1cc(Cn2cncn2)cc(C(C)(C)C#N)c1\tCHEMBL1399\n",
      "O=[N+]([O-])c1ccc(C(c2cc3ccc(O)cc3o2)n2cncn2)cc1\tCHEMBL202231\n",
      "N#Cc1ccc(C(c2cc3ccc(O)cc3o2)n2cncn2)cc1\tCHEMBL202226\n",
      "Oc1ccc2cc(C(c3ccc(Cl)cc3)n3cncn3)oc2c1\tCHEMBL446706\n",
      "CCc1ccc(C(c2cc3ccc(OC)cc3o2)n2cncn2)cc1\tCHEMBL371663\n",
      "COc1ccc2cc(C(c3ccc(C(F)(F)F)cc3)n3cncn3)oc2c1\tCHEMBL202065\n",
      "COc1ccc2cc(C(c3ccc(C)cc3)n3cncn3)oc2c1\tCHEMBL201831\n",
      "COc1ccc2cc(C(c3ccc([N+](=O)[O-])cc3)n3cncn3)oc2c1\tCHEMBL199334\n",
      "COc1ccc2cc(C(c3ccc(C#N)cc3)n3cncn3)oc2c1\tCHEMBL197650\n",
      "COc1ccc(C(c2cc3ccc(OC)cc3o2)n2cncn2)cc1\tCHEMBL379019\n",
      "COc1ccc2cc(C(c3ccc(Cl)cc3)n3cncn3)oc2c1\tCHEMBL382902\n",
      "COc1ccc2cc(C(c3ccc(F)cc3)n3cncn3)oc2c1\tCHEMBL198291\n",
      "COc1ccc(C(c2cc3ccc(OC)cc3o2)n2ccnc2)cc1\tCHEMBL382500\n",
      "COc1ccc(C(O)(c2cccnc2)c2cc3ccc(OC)cc3o2)cc1\tCHEMBL202020\n",
      "COc1ccc2cc(C(O)(c3ccc(Cl)cc3)c3cccnc3)oc2c1\tCHEMBL202612\n",
      "COc1ccc2cc(C(O)(c3ccc(F)cc3)c3cccnc3)oc2c1\tCHEMBL372999\n",
      "CS(=O)(=O)Nc1ccc([N+](=O)[O-])cc1OC1CCCCC1\tCHEMBL7162\n",
      "COc1cc([N+](=O)[O-])ccc1N(C)S(C)(=O)=O\tCHEMBL372835\n",
      "COc1cc([N+](=O)[O-])ccc1NS(C)(=O)=O\tCHEMBL381883\n",
      "CCCCCCOc1cc([N+](=O)[O-])ccc1N(C)S(C)(=O)=O\tCHEMBL202452\n",
      "CCCCCCOc1cc([N+](=O)[O-])ccc1NS(C)(=O)=O\tCHEMBL202471\n",
      "CCCCCCCCCOc1cc([N+](=O)[O-])ccc1N(C)S(C)(=O)=O\tCHEMBL202470\n",
      "CCCCCCCCCOc1cc([N+](=O)[O-])ccc1NS(C)(=O)=O\tCHEMBL202469\n",
      "CCC(CC)Oc1cc([N+](=O)[O-])ccc1N(C)S(C)(=O)=O\tCHEMBL202030\n",
      "CCC(CC)Oc1cc([N+](=O)[O-])ccc1NS(C)(=O)=O\tCHEMBL202474\n",
      "CN(c1ccc([N+](=O)[O-])cc1OC1CCCC1)S(C)(=O)=O\tCHEMBL202473\n",
      "CS(=O)(=O)Nc1ccc([N+](=O)[O-])cc1OC1CCCC1\tCHEMBL437497\n",
      "CN(c1ccc([N+](=O)[O-])cc1OCC1CCCCC1)S(C)(=O)=O\tCHEMBL204502\n",
      "CS(=O)(=O)Nc1ccc([N+](=O)[O-])cc1OCC1CCCCC1\tCHEMBL204501\n",
      "CC(C)Oc1cc([N+](=O)[O-])ccc1N(C)S(C)(=O)=O\tCHEMBL203869\n",
      "CC(C)Oc1cc([N+](=O)[O-])ccc1NS(C)(=O)=O\tCHEMBL274387\n",
      "CCCOc1cc([N+](=O)[O-])ccc1N(C)S(C)(=O)=O\tCHEMBL382482\n",
      "CCCOc1cc([N+](=O)[O-])ccc1NS(C)(=O)=O\tCHEMBL204184\n",
      "CN(c1ccc([N+](=O)[O-])cc1OC1CCCCC1)S(C)(=O)=O\tCHEMBL204183\n",
      "Clc1ccc(C(c2ccc3[nH]ccc3c2)n2ccnc2)cc1\tCHEMBL204039\n",
      "Cn1ccc2cc(C(c3ccc(Cl)cc3)n3ccnc3)ccc21\tCHEMBL382679\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "Clc1cccc(C(c2ccc3[nH]ccc3c2)n2ccnc2)c1\tCHEMBL204483\n",
      "N#Cc1ccc(C(c2ccc3[nH]ccc3c2)n2ccnc2)cc1\tCHEMBL205177\n",
      "CCn1ccc2cc(C(c3ccc(F)cc3)n3ccnc3)ccc21\tCHEMBL41761\n",
      "Fc1ccc(C(c2ccc3[nH]ccc3c2)n2ccnc2)cc1\tCHEMBL203981\n",
      "Clc1ccc(C(c2ccc3[nH]ccc3c2)n2ccnc2)cc1\tCHEMBL204039\n",
      "Clc1ccc(C(c2ccc3[nH]ccc3c2)n2ccnc2)cc1\tCHEMBL204039\n",
      "C1=C(c2cccnc2)Cc2ccccc21\tCHEMBL205782\n",
      "CC1CC(c2cccnc2)=Cc2ccccc21\tCHEMBL206070\n",
      "CC1Cc2ccccc2C=C1c1cccnc1\tCHEMBL206550\n",
      "CCC1=C(c2cccnc2)CCc2ccccc21\tCHEMBL205503\n",
      "CC1=C(c2cccnc2)CCc2ccccc21\tCHEMBL206283\n",
      "COc1ccc2c(c1)CCC(c1cccnc1)=C2\tCHEMBL206351\n",
      "COc1ccc2c(c1)C=C(c1cccnc1)C2\tCHEMBL206300\n",
      "C1=C(c2cccnc2)CCc2ccccc21\tCHEMBL205881\n",
      "N#Cc1ccc(C2CCCc3cncn32)cc1\tCHEMBL9298\n",
      "CC(=O)N1CCN(c2ccc(OCC3COC(Cn4ccnc4)(c4ccc(Cl)cc4Cl)O3)cc2)CC1\tCHEMBL157101\n",
      "COc1ccc(-c2oc3ccccc3c(=O)c2Cn2ccnc2)cc1\tCHEMBL212473\n",
      "COc1ccc(-c2oc3ccccc3c(=O)c2Cn2ccnc2)cc1\tCHEMBL212473\n",
      "N#Cc1ccc(C2CCCc3cncn32)cc1\tCHEMBL9298\n",
      "N#Cc1ccc(-c2oc3ccccc3c(=O)c2Cn2ccnc2)cc1\tCHEMBL209731\n",
      "O=c1c(Cn2ccnc2)c(-c2ccc(Br)cc2)oc2ccccc12\tCHEMBL377766\n",
      "O=c1c(Cn2ccnc2)c(-c2ccc([N+](=O)[O-])cc2)oc2ccccc12\tCHEMBL213588\n",
      "O=c1c(Cn2ccnc2)c(-c2ccc([N+](=O)[O-])cc2)oc2ccccc12\tCHEMBL213588\n",
      "O=c1c(Cn2ccnc2)c(-c2ccccc2)oc2ccccc12\tCHEMBL213587\n",
      "COc1ccc2c(=O)c(Cn3ccnc3)c(-c3ccc(C#N)cc3)oc2c1\tCHEMBL377626\n",
      "COc1ccc2c(=O)c(Cn3ccnc3)c(-c3ccc(C#N)cc3)oc2c1\tCHEMBL377626\n",
      "COc1ccc2c(=O)c(Cn3ccnc3)c(-c3ccc(Br)cc3)oc2c1\tCHEMBL209047\n",
      "COc1ccc2c(=O)c(Cn3ccnc3)c(-c3ccc(Br)cc3)oc2c1\tCHEMBL209047\n",
      "COc1ccc2c(=O)c(Cn3ccnc3)c(-c3ccc([N+](=O)[O-])cc3)oc2c1\tCHEMBL212168\n",
      "COc1ccc2c(=O)c(Cn3ccnc3)c(-c3ccc([N+](=O)[O-])cc3)oc2c1\tCHEMBL212168\n",
      "COc1ccc2c(=O)c(Cn3ccnc3)c(-c3ccccc3)oc2c1\tCHEMBL158635\n",
      "COc1ccc2c(=O)c(Cn3ccnc3)c(-c3ccccc3)oc2c1\tCHEMBL158635\n",
      "O=c1c(Cn2ccnc2)c(-c2ccccc2)oc2ccccc12\tCHEMBL213587\n",
      "O=c1c(Cn2ccnc2)c(-c2ccc(Br)cc2)oc2ccccc12\tCHEMBL377766\n",
      "N#Cc1ccc(-c2oc3ccccc3c(=O)c2Cn2ccnc2)cc1\tCHEMBL209731\n",
      "COc1cc(O)c2c(c1)O[C@]1(C)[C@@H](O)[C@@H](C)C[C@H](O)[C@@H]1C2=O\tCHEMBL376473\n",
      "CN(c1ccc([N+](=O)[O-])cc1OCc1ccccc1)S(C)(=O)=O\tCHEMBL222250\n",
      "CN(c1ccc([N+](=O)[O-])cc1OCc1ccc(F)cc1)S(C)(=O)=O\tCHEMBL376649\n",
      "Cc1ccc(COc2cc([N+](=O)[O-])ccc2N(C)S(C)(=O)=O)cc1\tCHEMBL222260\n",
      "CN(c1ccc([N+](=O)[O-])cc1OCc1ccccc1-c1ccccc1)S(C)(=O)=O\tCHEMBL223368\n",
      "CN(c1ccc([N+](=O)[O-])cc1OCc1ccc2ccccc2c1)S(C)(=O)=O\tCHEMBL374535\n",
      "CN(c1ccc([N+](=O)[O-])cc1OCc1ccc([N+](=O)[O-])cc1)S(C)(=O)=O\tCHEMBL376303\n",
      "CS(=O)(=O)Nc1ccc([N+](=O)[O-])cc1Oc1ccccc1\tCHEMBL56367\n",
      "CC(=O)N1CCN(c2ccc(OCC3COC(Cn4ccnc4)(c4ccc(Cl)cc4Cl)O3)cc2)CC1\tCHEMBL157101\n",
      "Cc1ncc2c(n1)-c1ccccc1C2n1ccnc1\tCHEMBL375292\n",
      "O=C1c2ccccc2-c2nnc(Cn3ccnc3)cc21\tCHEMBL387518\n",
      "c1ccc2c(c1)-c1ccccc1C2n1ccnc1\tCHEMBL225447\n",
      "c1ccc(C2(n3ccnc3)c3ccccc3-c3ccccc32)cc1\tCHEMBL223874\n",
      "COc1ccc2cc(Cn3ccnc3)c(=O)oc2c1\tCHEMBL387892\n",
      "O=c1cc(Cn2ccnc2)c2ccccc2o1\tCHEMBL427247\n",
      "COc1ccc2c(Cn3ccnc3)cc(=O)oc2c1\tCHEMBL225472\n",
      "COc1ccc2c(Cn3cncn3)cc(=O)oc2c1\tCHEMBL388604\n",
      "O=c1cc(OCCn2ccnc2)c2ccccc2o1\tCHEMBL225178\n",
      "O=c1cc(Cn2ccnc2)c2ccc(Oc3ccccc3)cc2o1\tCHEMBL437731\n",
      "COc1cc(Cn2ccnc2)c2ccc(=O)oc2c1\tCHEMBL375046\n",
      "O=c1ccc2cc(Cn3ccnc3)ccc2o1\tCHEMBL224228\n",
      "O=c1ccc2ccc(Cn3ccnc3)cc2o1\tCHEMBL224649\n",
      "O=c1oc2ccccc2c(Cn2ccnc2)c1-c1ccccc1\tCHEMBL224786\n",
      "COc1ccc2c(-c3ccccc3)c(Cn3ccnc3)c(=O)oc2c1\tCHEMBL224787\n",
      "O=c1cc(Cn2ccnc2)c2ccc(OCc3ccccc3)cc2o1\tCHEMBL225499\n",
      "S=c1ccc2cc(Cn3ccnc3)ccc2o1\tCHEMBL224600\n",
      "Cc1ncc2c(n1)-c1ccccc1C2n1ccnc1\tCHEMBL375292\n",
      "O=C1c2ccccc2-c2nnc(Cn3ccnc3)cc21\tCHEMBL387518\n",
      "c1ccc2c(c1)-c1ccccc1C2n1ccnc1\tCHEMBL225447\n",
      "c1ccc(C2(n3ccnc3)c3ccccc3-c3ccccc32)cc1\tCHEMBL223874\n",
      "c1ccc(C2(n3cncn3)c3ccccc3-c3ccccc32)cc1\tCHEMBL376371\n",
      "COc1ccc2cc(Cn3ccnc3)c(=O)oc2c1\tCHEMBL387892\n",
      "O=c1cc(Cn2ccnc2)c2ccccc2o1\tCHEMBL427247\n",
      "COc1ccc2c(Cn3ccnc3)cc(=O)oc2c1\tCHEMBL225472\n",
      "COc1ccc2c(Cn3cncn3)cc(=O)oc2c1\tCHEMBL388604\n",
      "O=c1cc(OCCn2ccnc2)c2ccccc2o1\tCHEMBL225178\n",
      "COc1ccc2c(OCCn3ccnc3)cc(=O)oc2c1\tCHEMBL375497\n",
      "O=c1cc(Cn2ccnc2)c2ccc(Oc3ccccc3)cc2o1\tCHEMBL437731\n",
      "COc1cc(Cn2ccnc2)c2ccc(=O)oc2c1\tCHEMBL375046\n",
      "O=c1ccc2cc(Cn3ccnc3)ccc2o1\tCHEMBL224228\n",
      "O=c1ccc2ccc(Cn3ccnc3)cc2o1\tCHEMBL224649\n",
      "O=c1ccc2ccc(Cn3cncn3)cc2o1\tCHEMBL223929\n",
      "O=c1ccc2ccc(OCCn3ccnc3)cc2o1\tCHEMBL390152\n",
      "COc1ccc2ccc(=O)oc2c1Cn1ccnc1\tCHEMBL223983\n",
      "O=c1oc2ccccc2c(Cn2ccnc2)c1-c1ccccc1\tCHEMBL224786\n",
      "COc1ccc2c(-c3ccccc3)c(Cn3ccnc3)c(=O)oc2c1\tCHEMBL224787\n",
      "O=c1cc(Cn2ccnc2)c2ccc(OCc3ccccc3)cc2o1\tCHEMBL225499\n",
      "S=c1ccc2cc(Cn3ccnc3)ccc2o1\tCHEMBL224600\n",
      "COc1ccc2c(c1)CCC(Cc1cnc[nH]1)=C2C\tCHEMBL390569\n",
      "c1ccc(C2(n3cncn3)c3ccccc3-c3ccccc32)cc1\tCHEMBL376371\n",
      "N#Cc1ccc([C@@H]2CCCc3cncn32)cc1\tCHEMBL31215\n",
      "O=c1c([C@@H](c2ccc([N+](=O)[O-])cc2)n2ccnc2)coc2ccccc12\tCHEMBL224665\n",
      "O=c1c([C@@H](c2ccc([N+](=O)[O-])cc2)n2ccnc2)coc2ccccc12\tCHEMBL224665\n",
      "N#Cc1ccc([C@@H]2CCCc3cncn32)cc1\tCHEMBL31215\n",
      "O=C1/C(=C/c2ccncc2)Cc2cc(O)ccc21\tCHEMBL26065\n",
      "N#Cc1ccc([C@@H](c2coc3ccccc3c2=O)n2ccnc2)cc1\tCHEMBL451201\n",
      "N#Cc1ccc([C@@H](c2coc3ccccc3c2=O)n2ccnc2)cc1\tCHEMBL451201\n",
      "N#Cc1ccc([C@H](c2coc3ccccc3c2=O)n2ccnc2)cc1\tCHEMBL225079\n",
      "N#Cc1ccc([C@H](c2coc3ccccc3c2=O)n2ccnc2)cc1\tCHEMBL225079\n",
      "O=c1c([C@H](c2ccc(Br)cc2)n2ccnc2)coc2ccccc12\tCHEMBL376786\n",
      "O=c1c([C@H](c2ccc(Br)cc2)n2ccnc2)coc2ccccc12\tCHEMBL376786\n",
      "O=c1c([C@@H](c2ccc(Br)cc2)n2ccnc2)coc2ccccc12\tCHEMBL522917\n",
      "O=c1c([C@@H](c2ccc(Br)cc2)n2ccnc2)coc2ccccc12\tCHEMBL522917\n",
      "O=c1c([C@H](c2ccc([N+](=O)[O-])cc2)n2ccnc2)coc2ccccc12\tCHEMBL525379\n",
      "O=c1c([C@H](c2ccc([N+](=O)[O-])cc2)n2ccnc2)coc2ccccc12\tCHEMBL525379\n",
      "COc1ccc2c(c1)C/C(=C/c1ccncc1)C2=O\tCHEMBL285155\n",
      "COc1ccc2c(c1)C/C(=C\\c1ccncc1)C2=O\tCHEMBL442262\n",
      "COc1cccc2c1C/C(=C/c1ccncc1)C2=O\tCHEMBL23334\n",
      "COc1cccc2c1C/C(=C\\c1ccncc1)C2=O\tCHEMBL288093\n",
      "O=C1/C(=C\\c2ccncc2)Cc2ccccc21\tCHEMBL285973\n",
      "O=C1/C(=C/c2ccncc2)Cc2ccccc21\tCHEMBL23150\n",
      "O=C1/C(=C/c2ccncc2)CCc2c1ccc1c2CCCC1\tCHEMBL376039\n",
      "O=C1/C(=C\\c2ccncc2)CCc2ccccc21\tCHEMBL388959\n",
      "COc1ccc2c(c1)C(=O)/C(=C/c1ccncc1)CC2\tCHEMBL419131\n",
      "O=C1/C(=C/c2ccncc2)CCc2cc(O)ccc21\tCHEMBL91992\n",
      "O=C1/C(=C/c2ccncc2)CCc2c(O)cccc21\tCHEMBL92488\n",
      "COc1ccc2c(c1)C(=O)/C(=C/c1ccncc1)CC2\tCHEMBL419131\n",
      "COc1ccc2c(c1)CC/C(=C\\c1ccncc1)C2=O\tCHEMBL328217\n",
      "COc1cccc2c1CC/C(=C\\c1ccncc1)C2=O\tCHEMBL91101\n",
      "O=C1/C(=C/c2ccncc2)CCc2ccccc21\tCHEMBL90585\n",
      "N#Cc1ccc(C(F)(c2ccc(C#N)cc2)n2ccnc2)cc1\tCHEMBL225378\n",
      "N#Cc1ccc(C(F)(c2ccc(C#N)cc2)n2ncnn2)cc1\tCHEMBL225360\n",
      "N#Cc1ccc(C(F)(c2ccc(C#N)cc2)n2ccnn2)cc1\tCHEMBL388515\n",
      "N#Cc1ccc(C(F)(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL225550\n",
      "CCC(c1ccc(C#N)cc1)(c1ccc(C#N)cc1)n1cncn1\tCHEMBL224462\n",
      "CC(c1ccc(C#N)cc1)(c1ccc(C#N)cc1)n1cncn1\tCHEMBL388187\n",
      "N#Cc1ccc(C(=C2CCCCC2)n2cncn2)cc1\tCHEMBL223555\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)c2cccnc2)cc1\tCHEMBL223554\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)c2cncnc2)cc1\tCHEMBL223452\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2ncnn2)cc1\tCHEMBL224790\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2ccnn2)cc1\tCHEMBL224789\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL1444\n",
      "N#Cc1ccc(C2(n3ccnc3)CCCC2)cc1\tCHEMBL225053\n",
      "CC(C)(c1ccc(C#N)cc1)n1ccnc1\tCHEMBL225128\n",
      "N#Cc1ccc(Cn2ccnc2)cc1\tCHEMBL349822\n",
      "O=[N+]([O-])c1ccc(Cn2ccnc2)cc1\tCHEMBL13784\n",
      "O=C(c1ccccc1)c1ccc(Cn2ccnc2)cc1\tCHEMBL223176\n",
      "Brc1ccc(Cn2ccnc2)cc1\tCHEMBL211207\n",
      "N#Cc1cccc(Cn2ccnc2)c1\tCHEMBL163922\n",
      "N#Cc1ccccc1Cn1ccnc1\tCHEMBL374934\n",
      "Cc1oc2c(C#N)ccc(Cn3ccnc3)c2c1C\tCHEMBL224391\n",
      "N#Cc1ccc(Cn2ccnc2)c2ccccc12\tCHEMBL375043\n",
      "N#Cc1ccc(/C(=N\\c2ccc(Br)cc2)n2ccnc2)cc1\tCHEMBL31456\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2ccnc2)cc1\tCHEMBL29482\n",
      "N#Cc1ccc(C2(c3ccc(C#N)cc3)CCCc3cncn32)cc1\tCHEMBL285809\n",
      "N#Cc1ccc2c(c1)CCC=C2n1ccnc1\tCHEMBL31224\n",
      "C=C(c1ccc(C#N)cc1)n1ccnc1\tCHEMBL31760\n",
      "N#Cc1ccc([C@H]2CCCc3cncn32)cc1\tCHEMBL287677\n",
      "O=c1c2ccccc2oc2cc(Cn3ccnc3)ccc12\tCHEMBL350922\n",
      "O=c1c2ccccc2oc2c(Br)ccc(Cn3ccnc3)c12\tCHEMBL78322\n",
      "O=c1c2ccccc2oc2c([N+](=O)[O-])ccc(Cn3ccnc3)c12\tCHEMBL307160\n",
      "N#Cc1ccc(Cn2ccnc2)c2c(=O)c3ccccc3oc12\tCHEMBL442373\n",
      "O=c1c2ccccc2oc2c(Cn3ccnc3)ccc(Br)c12\tCHEMBL421829\n",
      "O=c1c2ccccc2oc2c(Cn3ccnc3)ccc([N+](=O)[O-])c12\tCHEMBL159073\n",
      "N#Cc1ccc(Cn2ccnc2)c2oc3ccccc3c(=O)c12\tCHEMBL421149\n",
      "COc1ccc2c(=O)c(Cn3ccnc3)c(-c3ccccc3)oc2c1\tCHEMBL158635\n",
      "O=c1ccoc2cc(Cn3cncn3)ccc12\tCHEMBL223607\n",
      "O=c1ccoc2cc(Cn3ccnc3)ccc12\tCHEMBL158984\n",
      "O=c1ccoc2c(Cn3ccnc3)ccc(Br)c12\tCHEMBL161530\n",
      "O=c1ccoc2c(Cn3ccnc3)ccc([N+](=O)[O-])c12\tCHEMBL351167\n",
      "N#Cc1ccc(Cn2ccnc2)c2occc(=O)c12\tCHEMBL161230\n",
      "COc1cc2c(cc1Br)CC[C@@H]1[C@@H](c3ccncc3)[C@H]21\tCHEMBL2096873\n",
      "COc1cc2c(cc1Br)CC[C@H]1[C@H](c3ccncc3)[C@@H]21\tCHEMBL225497\n",
      "COc1ccc2c(c1)[C@@H]1[C@H](CC2)[C@H]1c1ccncc1\tCHEMBL2096872\n",
      "COc1ccc2c(c1)[C@H]1[C@@H](CC2)[C@@H]1c1ccncc1\tCHEMBL223717\n",
      "Cn1nnc2ccc([C@@H](c3ccc(Cl)cc3)n3cncn3)cc21\tCHEMBL519436\n",
      "Cn1nnc2ccc([C@H](c3ccc(Cl)cc3)n3cncn3)cc21\tCHEMBL224060\n",
      "Clc1cccc(C(c2ccc3nc[nH]c3c2)n2ccnc2)c1\tCHEMBL389433\n",
      "CC(C)(C#N)c1cc(Cn2cncn2)cc(C(C)(C)C#N)c1\tCHEMBL1399\n",
      "COCOc1ccc(C(=O)/C=C/c2ccc(OCOC)c(CC=C(C)C)c2)c(O)c1\tCHEMBL229944\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "COc1ccc2c(c1)OC(c1ccc(OCC=C(C)C)cc1)CC2=O\tCHEMBL228985\n",
      "COc1ccc(C(=O)/C=C/c2ccc(OCC=C(C)C)cc2)c(O)c1\tCHEMBL390364\n",
      "COc1ccc(C2CC(=O)c3ccc(OC)cc3O2)cc1\tCHEMBL229908\n",
      "COc1ccc(/C=C/C(=O)c2ccc(OC)cc2O)cc1\tCHEMBL229907\n",
      "COc1ccc2c(c1)OC(c1cccc(CC=C(C)C)c1)CC2=O\tCHEMBL229906\n",
      "COc1ccc(C(=O)/C=C/c2cccc(CC=C(C)C)c2)c(O)c1\tCHEMBL229905\n",
      "COc1ccc(C2CC(=O)c3ccccc3O2)cc1CC=C(C)C\tCHEMBL1162951\n",
      "COc1ccc(/C=C/C(O)c2ccccc2O)cc1CC=C(C)C\tCHEMBL388375\n",
      "COc1ccc2c(c1)OC(c1ccc(OC)c(CC=C(C)C)c1)CC2=O\tCHEMBL229635\n",
      "COc1ccc(C(=O)/C=C/c2ccc(OC)c(CC=C(C)C)c2)c(O)c1\tCHEMBL229633\n",
      "COc1ccc2c(c1)OC(c1ccc(O)c(CC=C(C)C)c1)CC2=O\tCHEMBL388374\n",
      "COc1ccc(C(=O)/C=C/c2ccc(O)c(CC=C(C)C)c2)c(O)c1\tCHEMBL229841\n",
      "COCOc1ccc(C2CC(=O)c3ccc(OC)cc3O2)cc1CC=C(C)C\tCHEMBL229790\n",
      "COCOc1ccc(/C=C/C(=O)c2ccc(OC)cc2O)cc1CC=C(C)C\tCHEMBL390731\n",
      "COc1ccc(C2CC(=O)c3ccc(O)cc3O2)cc1CC=C(C)C\tCHEMBL229789\n",
      "COc1ccc(/C=C/C(=O)c2ccc(O)cc2O)cc1CC=C(C)C\tCHEMBL389925\n",
      "COCOc1ccc2c(c1)OC(c1ccc(OC)c(CC=C(C)C)c1)CC2=O\tCHEMBL425670\n",
      "COCOc1ccc(C(=O)/C=C/c2ccc(OC)c(CC=C(C)C)c2)c(O)c1\tCHEMBL229893\n",
      "CC(C)=CCc1cc([C@@H]2CC(=O)c3ccc(O)cc3O2)ccc1O\tCHEMBL558103\n",
      "CC(C)=CCc1cc(/C=C/C(=O)c2ccc(O)cc2O)ccc1O\tCHEMBL229885\n",
      "COCOc1ccc2c(c1)OC(c1ccc(OCOC)c(CC=C(C)C)c1)CC2=O\tCHEMBL229945\n",
      "COc1ccc(C(=O)CC(O)c2ccc(OC)c(CC=C(C)C)c2)c(O)c1\tCHEMBL229094\n",
      "CC[C@H]1[C@@H]2[C@@H](O)CCN2C(=O)N1c1ccc(C#N)c(Cl)c1C\tCHEMBL229861\n",
      "N#Cc1ccc(N(Cc2ccc(Br)cc2)n2cnnc2)cc1\tCHEMBL108425\n",
      "N#Cc1ccc(N(Cc2ccc(C(F)(F)F)c(O)c2)n2cnnc2)cc1\tCHEMBL226999\n",
      "COc1ccc(CN(c2ccc(C#N)cc2)n2cnnc2)cc1O\tCHEMBL388093\n",
      "N#Cc1ccc(N(Cc2ccc(Br)c(O)c2)n2cnnc2)cc1\tCHEMBL226947\n",
      "N#Cc1ccc(N(Cc2ccc(Cl)c(O)c2)n2cnnc2)cc1\tCHEMBL226946\n",
      "N#Cc1ccc(N(Cc2ccc(F)c(O)c2)n2cnnc2)cc1\tCHEMBL390079\n",
      "N#Cc1ccc(N(Cc2cccc(O)c2)n2cnnc2)cc1\tCHEMBL390078\n",
      "COc1ccc(CN(c2ccc(C#N)cc2)n2cnnc2)cc1OS(N)(=O)=O\tCHEMBL226945\n",
      "N#Cc1ccc(N(Cc2ccc(Br)c(OS(N)(=O)=O)c2)n2cnnc2)cc1\tCHEMBL226893\n",
      "N#Cc1ccc(N(Cc2ccc(Cl)c(OS(N)(=O)=O)c2)n2cnnc2)cc1\tCHEMBL390077\n",
      "N#Cc1ccc(N(Cc2ccc(F)c(OS(N)(=O)=O)c2)n2cnnc2)cc1\tCHEMBL226892\n",
      "N#Cc1ccc(N(Cc2c(F)c(F)c(O)c(F)c2F)n2cnnc2)cc1\tCHEMBL226891\n",
      "N#Cc1ccc(N(Cc2cc(Cl)c(O)c(Cl)c2)n2cnnc2)cc1\tCHEMBL226843\n",
      "COc1cc(CN(c2ccc(C#N)cc2)n2cnnc2)cc(Cl)c1O\tCHEMBL388507\n",
      "N#Cc1ccc(N(Cc2ccc(O)c(C(F)(F)F)c2)n2cnnc2)cc1\tCHEMBL226842\n",
      "N#Cc1ccc(N(Cc2ccc(O)c(C#N)c2)n2cnnc2)cc1\tCHEMBL226841\n",
      "COc1cc(CN(c2ccc(C#N)cc2)n2cnnc2)ccc1O\tCHEMBL226791\n",
      "N#Cc1ccc(N(Cc2ccc(O)c(I)c2)n2cnnc2)cc1\tCHEMBL226790\n",
      "N#Cc1ccc(N(Cc2ccc(O)c(Br)c2)n2cnnc2)cc1\tCHEMBL388506\n",
      "N#Cc1ccc(N(Cc2ccc(O)c(Cl)c2)n2cnnc2)cc1\tCHEMBL226789\n",
      "N#Cc1ccc(N(Cc2ccc(O)c(F)c2)n2cnnc2)cc1\tCHEMBL226738\n",
      "N#Cc1ccc(N(Cc2ccc(O)cc2)n2cnnc2)cc1\tCHEMBL388291\n",
      "COc1cc(CN(c2ccc(C#N)cc2)n2cnnc2)cc(Cl)c1OS(N)(=O)=O\tCHEMBL390717\n",
      "N#Cc1ccc(N(Cc2ccc(OS(N)(=O)=O)c(C#N)c2)n2cnnc2)cc1\tCHEMBL226737\n",
      "COc1cc(CN(c2ccc(C#N)cc2)n2cnnc2)ccc1OS(N)(=O)=O\tCHEMBL226736\n",
      "N#Cc1ccc(N(Cc2ccc(OS(N)(=O)=O)c(I)c2)n2cnnc2)cc1\tCHEMBL390496\n",
      "O=C(c1ccccc1)c1ccccc1Cn1ccnc1\tCHEMBL244795\n",
      "N#Cc1ccc(C2CCCc3cncn32)cc1\tCHEMBL9298\n",
      "O=C(c1ccccc1)c1cc(Br)ccc1Cn1ccnc1\tCHEMBL242454\n",
      "O=C(c1ccc(-c2ccccc2)cc1)c1cc([N+](=O)[O-])ccc1Cn1ccnc1\tCHEMBL243756\n",
      "O=C(c1ccccc1)c1cc([N+](=O)[O-])ccc1Cn1ccnc1\tCHEMBL243755\n",
      "O=C(c1ccccc1)c1cc(Cn2ccnc2)ccc1[N+](=O)[O-]\tCHEMBL388930\n",
      "O=C(c1ccccc1)c1ccc(Cn2ccnc2)cc1\tCHEMBL223176\n",
      "O=C(c1ccc(-c2ccccc2)cc1)c1cccc(Cn2ccnc2)c1\tCHEMBL243961\n",
      "O=C(c1ccc(-c2ccccc2)cc1)c1ccccc1Cn1ccnc1\tCHEMBL243960\n",
      "O=C(c1ccccc1)c1cccc(Cn2ccnc2)c1\tCHEMBL397728\n",
      "O=C1CC(c2ccccc2)Oc2ccccc21\tCHEMBL274318\n",
      "O=c1cc(-c2ccccc2)oc2c1ccc1ccccc12\tCHEMBL283196\n",
      "O=C1CC(c2ccccc2)Oc2c1ccc1ccccc21\tCHEMBL404225\n",
      "O=C1CC(c2ccccc2)Oc2cc(O)ccc21\tCHEMBL97542\n",
      "COc1ccc2c(c1)OC(c1ccccc1)CC2=O\tCHEMBL9687\n",
      "O=C1CC(c2ccc(O)cc2)Oc2c1ccc1ccccc21\tCHEMBL256665\n",
      "O=C1CC(c2cccc(O)c2)Oc2c1ccc1ccccc21\tCHEMBL439241\n",
      "N#Cc1ccc(C2CC(=O)c3ccc4ccccc4c3O2)cc1\tCHEMBL255108\n",
      "N#Cc1cccc(C2CC(=O)c3ccc4ccccc4c3O2)c1\tCHEMBL258257\n",
      "O=C1C[C@@H](c2ccc(O)cc2)Oc2cc(O)ccc21\tCHEMBL252642\n",
      "O=C1CC(c2cccc(O)c2)Oc2cc(O)ccc21\tCHEMBL255110\n",
      "COc1ccc2c(c1)OC(c1ccc(O)cc1)CC2=O\tCHEMBL402970\n",
      "COc1ccc2c(c1)OC(c1cccc(O)c1)CC2=O\tCHEMBL256407\n",
      "CS(=O)(=O)Nc1ccc([N+](=O)[O-])cc1OC1CCCCC1\tCHEMBL7162\n",
      "CN(c1ccc(NC(=O)c2ccc(-c3ccccc3)cc2)cc1OCc1ccccc1)S(C)(=O)=O\tCHEMBL271794\n",
      "CN(c1ccc(NC(=O)c2ccc3ccccc3c2)cc1OCc1ccccc1)S(C)(=O)=O\tCHEMBL270283\n",
      "CN(c1ccc(NC(=O)c2ccc(C#N)cc2)cc1OCc1ccccc1)S(C)(=O)=O\tCHEMBL409271\n",
      "CN(c1ccc(NC(=O)c2ccc(Cl)c([N+](=O)[O-])c2)cc1OCc1ccccc1)S(C)(=O)=O\tCHEMBL270915\n",
      "CN(c1ccc(NC(=O)c2ccc(Cl)c(Cl)c2)cc1OCc1ccccc1)S(C)(=O)=O\tCHEMBL271129\n",
      "CN(c1ccc(NC(=O)C2CCCCC2)cc1OCc1ccccc1)S(C)(=O)=O\tCHEMBL270917\n",
      "CN(c1ccc(NC(=O)c2ccccc2)cc1OCc1ccccc1)S(C)(=O)=O\tCHEMBL443066\n",
      "CC(=O)Nc1ccc(N(C)S(C)(=O)=O)c(OCc2ccccc2)c1\tCHEMBL272456\n",
      "CN(c1ccc(N)cc1OCc1ccccc1)S(C)(=O)=O\tCHEMBL272398\n",
      "Cc1ccc(S(=O)(=O)Nc2ccc([N+](=O)[O-])cc2OCc2ccccc2)cc1\tCHEMBL272464\n",
      "O=[N+]([O-])c1ccc(S(=O)(=O)Nc2ccc([N+](=O)[O-])cc2OCc2ccccc2)cc1\tCHEMBL405881\n",
      "O=C(Nc1ccc([N+](=O)[O-])cc1OCc1ccccc1)c1ccc(-c2ccccc2)cc1\tCHEMBL270488\n",
      "O=C(Nc1ccc([N+](=O)[O-])cc1OCc1ccccc1)c1ccc2ccccc2c1\tCHEMBL269877\n",
      "N#Cc1ccc(C(=O)Nc2ccc([N+](=O)[O-])cc2OCc2ccccc2)cc1\tCHEMBL407092\n",
      "O=C(Nc1ccc([N+](=O)[O-])cc1OCc1ccccc1)c1ccc(Cl)c(Cl)c1\tCHEMBL273049\n",
      "O=C(Nc1ccc([N+](=O)[O-])cc1OCc1ccccc1)c1ccc(Cl)c([N+](=O)[O-])c1\tCHEMBL260098\n",
      "O=C(Nc1ccc([N+](=O)[O-])cc1OCc1ccccc1)c1cccc([N+](=O)[O-])c1\tCHEMBL270301\n",
      "O=C(Nc1ccc([N+](=O)[O-])cc1OCc1ccccc1)c1ccc([N+](=O)[O-])cc1\tCHEMBL409448\n",
      "CC(=O)Nc1ccc([N+](=O)[O-])cc1OCc1ccccc1\tCHEMBL404981\n",
      "O=C(Nc1ccc([N+](=O)[O-])cc1OCc1ccccc1)C1CCCCC1\tCHEMBL260955\n",
      "O=C(Nc1ccc([N+](=O)[O-])cc1OCc1ccccc1)c1ccccc1\tCHEMBL407953\n",
      "CN(c1ccc([N+](=O)[O-])cc1OCc1cccc2ccccc12)S(C)(=O)=O\tCHEMBL222189\n",
      "CS(=O)(=O)Nc1ccc([N+](=O)[O-])cc1OCc1cccc2ccccc12\tCHEMBL220885\n",
      "CN(c1ccc([N+](=O)[O-])cc1OCCc1ccccc1)S(C)(=O)=O\tCHEMBL436515\n",
      "CS(=O)(=O)Nc1ccc([N+](=O)[O-])cc1OCCc1ccccc1\tCHEMBL374651\n",
      "CN(c1ccc([N+](=O)[O-])cc1OCc1ccc(-c2ccccc2)cc1)S(C)(=O)=O\tCHEMBL373377\n",
      "CS(=O)(=O)Nc1ccc([N+](=O)[O-])cc1OCc1ccc(-c2ccccc2)cc1\tCHEMBL426541\n",
      "CN(c1ccc([N+](=O)[O-])cc1OCc1ccc(Br)cc1)S(C)(=O)=O\tCHEMBL220793\n",
      "CS(=O)(=O)Nc1ccc([N+](=O)[O-])cc1OCc1ccc(Br)cc1\tCHEMBL220829\n",
      "CN(c1ccc([N+](=O)[O-])cc1OCc1ccc(Cl)cc1)S(C)(=O)=O\tCHEMBL220792\n",
      "CS(=O)(=O)Nc1ccc([N+](=O)[O-])cc1OCc1ccc(Cl)cc1\tCHEMBL220774\n",
      "CN(c1ccc([N+](=O)[O-])cc1OCc1ccc(F)cc1)S(C)(=O)=O\tCHEMBL376649\n",
      "CS(=O)(=O)Nc1ccc([N+](=O)[O-])cc1OCc1ccc(F)cc1\tCHEMBL373427\n",
      "CC(C)c1ccc(COc2cc([N+](=O)[O-])ccc2NS(C)(=O)=O)cc1\tCHEMBL223421\n",
      "COc1ccc(COc2cc([N+](=O)[O-])ccc2N(C)S(C)(=O)=O)cc1\tCHEMBL224037\n",
      "COc1ccc(COc2cc([N+](=O)[O-])ccc2NS(C)(=O)=O)cc1\tCHEMBL223369\n",
      "Cc1ccc(COc2cc([N+](=O)[O-])ccc2N(C)S(C)(=O)=O)cc1\tCHEMBL222260\n",
      "Cc1ccc(COc2cc([N+](=O)[O-])ccc2NS(C)(=O)=O)cc1\tCHEMBL387386\n",
      "CN(c1ccc([N+](=O)[O-])cc1OCc1ccccc1-c1ccccc1)S(C)(=O)=O\tCHEMBL223368\n",
      "CS(=O)(=O)Nc1ccc([N+](=O)[O-])cc1OCc1ccccc1-c1ccccc1\tCHEMBL223162\n",
      "CN(c1ccc([N+](=O)[O-])cc1OCc1ccc2ccccc2c1)S(C)(=O)=O\tCHEMBL374535\n",
      "CS(=O)(=O)Nc1ccc([N+](=O)[O-])cc1OCc1ccc2ccccc2c1\tCHEMBL223161\n",
      "CN(c1ccc([N+](=O)[O-])cc1OCc1ccc([N+](=O)[O-])cc1)S(C)(=O)=O\tCHEMBL376303\n",
      "CS(=O)(=O)Nc1ccc([N+](=O)[O-])cc1OCc1ccc([N+](=O)[O-])cc1\tCHEMBL223109\n",
      "CN(c1ccc([N+](=O)[O-])cc1OCc1ccccc1)S(C)(=O)=O\tCHEMBL222250\n",
      "CS(=O)(=O)Nc1ccc([N+](=O)[O-])cc1OCc1ccccc1\tCHEMBL223108\n",
      "CC(C)Oc1cc([N+](=O)[O-])ccc1N(C)S(C)(=O)=O\tCHEMBL203869\n",
      "CC(C)Oc1cc([N+](=O)[O-])ccc1NS(C)(=O)=O\tCHEMBL274387\n",
      "CCCCCCCCCOc1cc([N+](=O)[O-])ccc1N(C)S(C)(=O)=O\tCHEMBL202470\n",
      "CCCCCCCCCOc1cc([N+](=O)[O-])ccc1NS(C)(=O)=O\tCHEMBL202469\n",
      "CCCCCCOc1cc([N+](=O)[O-])ccc1N(C)S(C)(=O)=O\tCHEMBL202452\n",
      "CCCCCCOc1cc([N+](=O)[O-])ccc1NS(C)(=O)=O\tCHEMBL202471\n",
      "CCCOc1cc([N+](=O)[O-])ccc1N(C)S(C)(=O)=O\tCHEMBL382482\n",
      "CCCOc1cc([N+](=O)[O-])ccc1NS(C)(=O)=O\tCHEMBL204184\n",
      "CCC(CC)Oc1cc([N+](=O)[O-])ccc1N(C)S(C)(=O)=O\tCHEMBL202030\n",
      "CCC(CC)Oc1cc([N+](=O)[O-])ccc1NS(C)(=O)=O\tCHEMBL202474\n",
      "CN(c1ccc([N+](=O)[O-])cc1OCC1CCCCC1)S(C)(=O)=O\tCHEMBL204502\n",
      "CS(=O)(=O)Nc1ccc([N+](=O)[O-])cc1OCC1CCCCC1\tCHEMBL204501\n",
      "CN(c1ccc([N+](=O)[O-])cc1OC1CCCC1)S(C)(=O)=O\tCHEMBL202473\n",
      "CS(=O)(=O)Nc1ccc([N+](=O)[O-])cc1OC1CCCC1\tCHEMBL437497\n",
      "COc1cc([N+](=O)[O-])ccc1N(C)S(C)(=O)=O\tCHEMBL372835\n",
      "COc1cc([N+](=O)[O-])ccc1NS(C)(=O)=O\tCHEMBL381883\n",
      "CN(c1ccc([N+](=O)[O-])cc1OC1CCCCC1)S(C)(=O)=O\tCHEMBL204183\n",
      "Cc1ccc(C)c(COc2cc([N+](=O)[O-])ccc2NS(C)(=O)=O)c1\tCHEMBL220886\n",
      "COc1ccc2c(c1)C=C(c1cccnc1)C2\tCHEMBL206300\n",
      "COc1ccc2c(c1)CCC1C(c3ccncc3)C21\tCHEMBL67842\n",
      "COc1ccc([C@](O)(c2cccnc2)c2cc3ccc(OC)cc3o2)cc1\tCHEMBL3084324\n",
      "COc1ccc2c(Cn3ccnc3)cc(=O)oc2c1\tCHEMBL225472\n",
      "COc1ccc2cc(C(c3ccc(C)cc3)n3cncn3)oc2c1\tCHEMBL201831\n",
      "C1=C(Cn2ccnc2)c2cccnc2CC1\tCHEMBL53068\n",
      "Oc1cccc2c1CCC1C(c3ccncc3)C21\tCHEMBL65590\n",
      "c1ccc2c(c1)CC(Cc1ccncc1)C2\tCHEMBL24593\n",
      "c1ccc2c(c1)-c1ccccc1C2n1ccnc1\tCHEMBL225447\n",
      "Oc1ccc2cc(-c3cccnc3)ccc2c1\tCHEMBL195571\n",
      "c1cc2ccc(Cn3ccnc3)cc2cn1\tCHEMBL298720\n",
      "O=c1ccoc2cc(Cn3cnnc3)ccc12\tCHEMBL157925\n",
      "c1ccc2cc(-n3ccnc3)ccc2c1\tCHEMBL195996\n",
      "COc1ccc2c(c1)CCC1C(c3cnc[nH]3)C21\tCHEMBL438549\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "CC1=C[C@@H]2c3c(O)cc(-c4cc5ccc(O)cc5o4)cc3O[C@]3(c4ccc(O)cc4O)Oc4cc(O)ccc4[C@@H](C1)C23\tCHEMBL454705\n",
      "CC(C)=CCc1cc2cc(-c3cc(O)cc(O)c3)oc2cc1O\tCHEMBL465881\n",
      "CC(C)=CCc1c(O)ccc(C(=O)/C=C/c2ccc(O)cc2O)c1O\tCHEMBL465880\n",
      "COc1ccc(CCCc2ccc(O)cc2)c(O)c1\tCHEMBL465879\n",
      "O=C1C[C@@H](c2ccc(O)cc2)Oc2cc(O)cc(O)c21\tCHEMBL9352\n",
      "CC(C)=CCc1cc(-c2oc3c(CC=C(C)C)c(O)cc(O)c3c(=O)c2O)ccc1O\tCHEMBL464007\n",
      "CC(C)=CCc1c(O)ccc2c1O[C@H](c1ccc(O)cc1O)CC2=O\tCHEMBL457686\n",
      "O=C1C[C@@H](c2ccc(O)cc2O)Oc2cc(O)cc(O)c21\tCHEMBL458395\n",
      "CC(C)=CCc1cc(C2CC(=O)c3c(O)cc(O)cc3O2)ccc1O\tCHEMBL457680\n",
      "CC(C)=CCc1cc(-c2oc3cc(O)cc(O)c3c(=O)c2O)ccc1O\tCHEMBL457679\n",
      "CC(C)(O)C1Cc2c(ccc3c2O[C@H](c2ccc(O)cc2O)CC3=O)O1\tCHEMBL457678\n",
      "CC(C)=CCc1c(O)cc(O)cc1-c1cc2ccc(O)cc2o1\tCHEMBL458188\n",
      "C/C(=C\\Cc1c(O)ccc(C(=O)/C=C/c2ccc(O)cc2O)c1O)COC(=O)/C=C/c1ccc(O)cc1\tCHEMBL514745\n",
      "COc1cc(/C=C/C(=O)OC/C(C)=C/Cc2c(O)ccc(C(=O)/C=C/c3ccc(O)cc3O)c2O)ccc1O\tCHEMBL463638\n",
      "CC(C)=CCC/C(C)=C/Cc1c(-c2ccc(O)cc2O)oc2cc(O)cc(O)c2c1=O\tCHEMBL458187\n",
      "N#Cc1ccc(C2CCCc3cncn32)cc1\tCHEMBL9298\n",
      "C[C@@H]1[C@H]2C3=CC[C@@H]4[C@@]5(C)CC[C@H](O)C(C)(C)[C@@H]5CC[C@@]4(C)[C@]3(C)CC[C@@]2(C(=O)O)CC[C@H]1C\tCHEMBL169\n",
      "C[C@@H]1[C@H]2C3=CC[C@@H]4[C@@]5(C)CCC(=O)C(C)(C)[C@@H]5CC[C@@]4(C)[C@]3(C)CC[C@@]2(C(=O)O)CC[C@H]1C\tCHEMBL487887\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=C(O)C(=O)CC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL132530\n",
      "C[C@@H]1[C@H]2C3=CC[C@@H]4[C@@]5(C)CC[C@H](O)C(C)(C)[C@@H]5CC[C@@]4(C)[C@]3(C)CC[C@@]2(C(=O)NCCCN2CCN(CCCN)CC2)CC[C@H]1C\tCHEMBL264924\n",
      "CC(=O)O[C@H]1CC[C@@]2(C)[C@@H](CC[C@]3(C)[C@@H]2CC=C2[C@@H]4[C@@H](C)[C@H](C)CC[C@]4(C(=O)NCCCN4CCN(CCCN)CC4)CC[C@]23C)C1(C)C\tCHEMBL270215\n",
      "C[C@@H]1[C@H]2C3=CC[C@@H]4[C@@]5(C)CCC(=O)NC(C)(C)[C@@H]5CC[C@@]4(C)[C@]3(C)CC[C@@]2(C(=O)O)CC[C@H]1C\tCHEMBL454421\n",
      "C=C(C)[C@@H]1CC[C@]2(C)[C@H](CC=C3[C@@H]4[C@@H](C)[C@H](C)CC[C@]4(C(=O)O)CC[C@]32C)[C@@]1(C)CCC#N\tCHEMBL486853\n",
      "C[C@@H]1[C@H]2C3=CC[C@@H]4[C@@]5(C)CC/C(=N\\O)C(C)(C)[C@@H]5CC[C@@]4(C)[C@]3(C)CC[C@@]2(C(=O)O)CC[C@H]1C\tCHEMBL487888\n",
      "O=c1cc(-c2ccc(O)cc2)oc2cc(O)cc(O)c12\tCHEMBL28\n",
      "COc1ccc(C(Cn2ccnc2)Sc2ccc(Cl)c(Cl)c2)cc1\tCHEMBL459502\n",
      "Oc1cc2c(cc1C(c1ccc3c(c1)OCO3)n1ccnc1)OCO2\tCHEMBL459503\n",
      "Oc1cc2c(cc1C(c1ccc(F)cc1)n1ccnc1)OCO2\tCHEMBL513615\n",
      "O=Cc1c(Cn2ccnc2)c2cc([N+](=O)[O-])ccc2n1Cc1ccccc1\tCHEMBL496777\n",
      "Cc1nc2ccc(Cl)c(-n3ccnc3)c2cc1CCn1ccnc1\tCHEMBL495565\n",
      "COc1ccc(-c2cnnn2Cc2ccco2)cc1\tCHEMBL495563\n",
      "c1ccc(-c2cc(-c3cccnc3)nc(-c3cccnc3)c2)cc1\tCHEMBL495566\n",
      "COc1ccc2nc3cc(Cl)ccc3c(Nc3cccnc3)c2c1\tCHEMBL495567\n",
      "COc1ccc2c(Cc3ccc(O)cc3)cncc2c1O\tCHEMBL523231\n",
      "COc1ccc2c(Cc3cccc([N+](=O)[O-])c3)cncc2c1O\tCHEMBL495777\n",
      "O=[N+]([O-])c1ccc2c(c1)nc(-c1ccccn1)n2CCc1ccncc1\tCHEMBL523232\n",
      "Oc1ccc(-c2nc(-c3cccnc3)c(-c3ccc(Cl)cc3)[nH]2)cc1\tCHEMBL509750\n",
      "COc1ccc(/C(=N/Nc2cnnc(Cl)c2Cl)c2ccccc2)cc1\tCHEMBL497614\n",
      "O=[N+]([O-])c1cccc(Cc2nncc3ccccc23)c1\tCHEMBL495574\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL1444\n",
      "O=c1c([C@@H](c2ccc([N+](=O)[O-])cc2)n2ccnc2)coc2ccccc12\tCHEMBL224665\n",
      "O=c1c2ccccc2oc2c(Cn3ccnc3)ccc([N+](=O)[O-])c12\tCHEMBL159073\n",
      "N#Cc1ccc(Cn2ccnc2)c2oc3ccccc3c(=O)c12\tCHEMBL421149\n",
      "O=c1c(Cn2ccnc2)c(-c2ccc([N+](=O)[O-])cc2)oc2ccccc12\tCHEMBL213588\n",
      "O=c1c([C@@H](c2ccc(Br)cc2)n2ccnc2)coc2ccccc12\tCHEMBL522917\n",
      "O=c1cc(Cn2ccnc2)c2ccc(Oc3ccccc3)cc2o1\tCHEMBL437731\n",
      "N#Cc1ccc(-c2oc3ccccc3c(=O)c2Cn2ccnc2)cc1\tCHEMBL209731\n",
      "O=c1c(Cn2ccnc2)c(-c2ccccc2)oc2ccccc12\tCHEMBL213587\n",
      "c1ccc(C2(n3ccnc3)c3ccccc3-c3ccccc32)cc1\tCHEMBL223874\n",
      "COc1ccc(-c2oc3ccccc3c(=O)c2Cn2ccnc2)cc1\tCHEMBL212473\n",
      "O=c1c2ccccc2oc2c(Cn3ccnc3)ccc(Br)c12\tCHEMBL421829\n",
      "O=c1c([C@H](c2ccc([N+](=O)[O-])cc2)n2ccnc2)coc2ccccc12\tCHEMBL525379\n",
      "O=c1ccoc2cc(Cn3ccnc3)ccc12\tCHEMBL158984\n",
      "COc1ccc2c(-c3ccccc3)c(Cn3ccnc3)c(=O)oc2c1\tCHEMBL224787\n",
      "N#Cc1ccc([C@H](c2coc3ccccc3c2=O)n2ccnc2)cc1\tCHEMBL225079\n",
      "O=c1ccc2cc(Cn3ccnc3)ccc2o1\tCHEMBL224228\n",
      "O=c1cc(Cn2ccnc2)c2ccc(OCc3ccccc3)cc2o1\tCHEMBL225499\n",
      "COc1cc(Cn2ccnc2)c2ccc(=O)oc2c1\tCHEMBL375046\n",
      "C1=C(Cn2ccnc2)c2cccnc2CC1\tCHEMBL53068\n",
      "O=c1c(-c2ccccc2)c(SCc2ccncc2)oc2cc(OCc3ccccc3)ccc12\tCHEMBL188664\n",
      "COc1ccc(-c2cc3c(=O)cc(O)oc3cc2SCc2ccncc2)cc1\tCHEMBL496375\n",
      "C1=C(CCCn2ccnc2)c2cccnc2CC1\tCHEMBL53290\n",
      "N#Cc1ccc(Cn2ccnc2)c2occc(=O)c12\tCHEMBL161230\n",
      "O=c1c(-c2ccc(O)cc2)c(SCc2ccncc2)oc2cc(O)ccc12\tCHEMBL185950\n",
      "COc1ccc2c(Cn3ccnc3)cc(=O)oc2c1\tCHEMBL225472\n",
      "O=c1c([C@H](c2ccc(Br)cc2)n2ccnc2)coc2ccccc12\tCHEMBL376786\n",
      "c1cc(CCn2ccnc2)c2cccnc2c1\tCHEMBL54803\n",
      "O=c1c2ccccc2oc2cc(Cn3ccnc3)ccc12\tCHEMBL350922\n",
      "O=c1c(Cn2ccnc2)c(-c2ccc(Br)cc2)oc2ccccc12\tCHEMBL377766\n",
      "COc1ccc2c(=O)c(Cn3ccnc3)c(-c3ccc([N+](=O)[O-])cc3)oc2c1\tCHEMBL212168\n",
      "C1=C(CCn2ccnc2)c2cccnc2CC1\tCHEMBL293122\n",
      "O=c1c(-c2ccccc2)c(-n2ccnc2)oc2cc(OCc3ccccc3)ccc12\tCHEMBL363493\n",
      "COc1ccc(-c2c(SCc3ccncc3)oc3cc(OCc4ccccc4)ccc3c2=O)cc1\tCHEMBL186079\n",
      "COc1ccc2c(=O)c(Cn3ccnc3)c(-c3ccccc3)oc2c1\tCHEMBL158635\n",
      "O=c1c(-c2ccccc2)c(SCc2ccncc2)oc2cc(O)ccc12\tCHEMBL184749\n",
      "N#Cc1ccc([C@@H](c2coc3ccccc3c2=O)n2ccnc2)cc1\tCHEMBL451201\n",
      "O=c1ccc2ccc(Cn3ccnc3)cc2o1\tCHEMBL224649\n",
      "C(\\Cn1ccnc1)=C1\\CCCc2sccc21\tCHEMBL57513\n",
      "O=c1cc(OCCn2ccnc2)c2ccccc2o1\tCHEMBL225178\n",
      "COc1ccc2c(=O)c(-c3ccccc3)c(-n3ccnc3)oc2c1\tCHEMBL192306\n",
      "O=c1c2ccccc2oc2c(Br)ccc(Cn3ccnc3)c12\tCHEMBL78322\n",
      "O=c1c2ccccc2oc2c([N+](=O)[O-])ccc(Cn3ccnc3)c12\tCHEMBL307160\n",
      "O=c1ccoc2c(Cn3ccnc3)ccc([N+](=O)[O-])c12\tCHEMBL351167\n",
      "O=c1ccc2cc(Cn3ccnc3)ccc2o1\tCHEMBL224228\n",
      "c1cc2ccc(Cn3ccnc3)cc2cn1\tCHEMBL298720\n",
      "COc1ccc2c(=O)c(-c3ccccc3)c(SCc3ccncc3)oc2c1\tCHEMBL185809\n",
      "COc1ccc2c(=O)c(Cn3ccnc3)c(-c3ccc(C#N)cc3)oc2c1\tCHEMBL377626\n",
      "COc1ccc(-c2c(-n3ccnc3)oc3cc(OC)ccc3c2=O)cc1\tCHEMBL189472\n",
      "O=c1cc(Cn2ccnc2)c2ccccc2o1\tCHEMBL427247\n",
      "O=c1ccoc2c(Cn3ccnc3)ccc(Br)c12\tCHEMBL161530\n",
      "N#Cc1ccc(Cn2ccnc2)c2c(=O)c3ccccc3oc12\tCHEMBL442373\n",
      "C(\\Cn1ccnc1)=C1/CCCc2occc21\tCHEMBL56059\n",
      "COc1ccc2cc(Cn3ccnc3)c(=O)oc2c1\tCHEMBL387892\n",
      "c1ccc2c(c1)-c1ccccc1C2n1ccnc1\tCHEMBL225447\n",
      "COc1ccc2c(=O)c(-c3ccc(C)cc3)c(SCc3ccncc3)oc2c1\tCHEMBL184051\n",
      "c1cc2cc(-n3ccnc3)ccc2cn1\tCHEMBL299066\n",
      "COc1ccc(-c2c(SCc3ccncc3)oc3cc(OC)ccc3c2=O)cc1\tCHEMBL361653\n",
      "c1cc(CCn2ccnc2)c2ccsc2c1\tCHEMBL460382\n",
      "O=c1c(-c2ccccc2)c(SCc2cccnc2)oc2cc(O)ccc12\tCHEMBL364326\n",
      "COc1ccc2c(Cn3cncn3)cc(=O)oc2c1\tCHEMBL388604\n",
      "O=c1ccoc2cc(Cn3cnnc3)ccc12\tCHEMBL157925\n",
      "C(\\Cn1ccnc1)=C1/CCCc2sccc21\tCHEMBL55558\n",
      "c1ccc(C2(n3cncn3)c3ccccc3-c3ccccc32)cc1\tCHEMBL376371\n",
      "COc1ccc2c(=O)c(Cn3ccnc3)c(-c3ccc(Br)cc3)oc2c1\tCHEMBL209047\n",
      "COc1ccc(-c2c(-n3ccnc3)oc3cc(OCc4ccccc4)ccc3c2=O)cc1\tCHEMBL189274\n",
      "O=c1oc2ccccc2c(Cn2ccnc2)c1-c1ccccc1\tCHEMBL224786\n",
      "COc1ccc2c(=O)c(-c3ccccc3)c(SCc3cccnc3)oc2c1\tCHEMBL185819\n",
      "COc1ccc2c(c1)CCC(CC1C=NC=N1)=C2C\tCHEMBL448463\n",
      "COc1ccc2c(=O)c(-c3ccccc3)c(-n3cncn3)oc2c1\tCHEMBL190099\n",
      "O=C1c2ccccc2-c2nnc(Cn3ccnc3)cc21\tCHEMBL387518\n",
      "COc1ccc2c(=O)c(-c3ccccc3)c(Sc3nnc[nH]3)oc2c1\tCHEMBL190208\n",
      "COc1ccc2c(=O)c(-c3ccccc3)c(Sc3ncc[nH]3)oc2c1\tCHEMBL191488\n",
      "COc1ccc2c(=O)c(-c3ccccc3)c(SCc3ccccn3)oc2c1\tCHEMBL362906\n",
      "COc1ccc(C(c2cc3c(cc2O)OCO3)n2ccnc2)cc1\tCHEMBL522733\n",
      "Cc1cc(=O)[nH]c2c3c(ccc12)OCO3\tCHEMBL482693\n",
      "Cc1cc(=O)n(C)c2c1C(=O)C=CC2=O\tCHEMBL450666\n",
      "COc1cc(OC)c2[nH]c(=O)cc(C)c2c1\tCHEMBL476126\n",
      "Cc1cc(=O)[nH]c2ccccc12\tCHEMBL424414\n",
      "Cc1cc(=O)n(C)c2ccccc12\tCHEMBL60376\n",
      "COc1cccc2c(C)cc(=O)[nH]c12\tCHEMBL453417\n",
      "COc1ccc2c(C)cc(=O)[nH]c2c1OC\tCHEMBL503538\n",
      "COc1ccc(OC)c2c(C)cc(=O)[nH]c12\tCHEMBL493174\n",
      "COc1cc2c(C)cc(=O)[nH]c2c(OC)c1OC\tCHEMBL454226\n",
      "COc1cc(OC)c2[nH]c(=O)cc(C)c2c1OC\tCHEMBL493181\n",
      "COc1ccc2c(C)cc(=O)n(C)c2c1OC\tCHEMBL453233\n",
      "COc1c(OC)c(OC)c2c(C)cc(=O)[nH]c2c1OC\tCHEMBL493182\n",
      "COc1cc(=O)n(C)c2c3c(ccc12)OCO3\tCHEMBL492762\n",
      "COc1cc2c(C)cc(=O)n(C)c2c(OC)c1OC\tCHEMBL492382\n",
      "COc1cccc2c(C)cc(=O)n(C)c12\tCHEMBL506858\n",
      "Cc1cc(=O)n(C)c2c3c(ccc12)OCO3\tCHEMBL492763\n",
      "COc1ccc(OC)c2c1c(C)cc(=O)n2C\tCHEMBL490867\n",
      "COc1cc(OC)c2c(c1)c(C)cc(=O)n2C\tCHEMBL493173\n",
      "COc1c(OC)c(OC)c2c(c(C)cc(=O)n2C)c1OC\tCHEMBL455593\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "COc1cc(OC)c2c(c(C)cc(=O)n2C)c1OC\tCHEMBL449065\n",
      "COc1ccc(Cc2cc3cc(OC)ccc3cc2-c2cccnc2)cc1\tCHEMBL494713\n",
      "N#Cc1ccc(C2CCCc3cncn32)cc1\tCHEMBL9298\n",
      "COc1c(O)cc2oc3cc(O)c(CC=C(C)C)c(O)c3c(=O)c2c1CCC(C)(C)O\tCHEMBL462879\n",
      "CC(C)=CCc1c(O)cc2oc3cc(O)c(O)c(CC=C(C)C)c3c(=O)c2c1O\tCHEMBL260553\n",
      "COc1c(O)cc2oc3cc(O)c(CC=C(C)C)c(O)c3c(=O)c2c1CC=C(C)C\tCHEMBL323197\n",
      "CC(C)=CCc1c(O)cc2oc3c(CC=C(C)C)c(O)c(O)c(CC=C(C)C)c3c(=O)c2c1O\tCHEMBL454580\n",
      "CC(C)=CCc1c(O)cc2oc3cc(O)c(O)c(CC=C(C)C)c3c(=O)c2c1O\tCHEMBL260553\n",
      "CC(C)=CCc1cc(C2CC(=O)c3ccc(O)cc3O2)ccc1O\tCHEMBL389924\n",
      "CC(C)=CCc1cc(C2CC(=O)c3ccc(O)cc3O2)ccc1O\tCHEMBL389924\n",
      "CC(C)=CCc1cc(C2CC(=O)c3ccc(O)cc3O2)ccc1O\tCHEMBL389924\n",
      "C[C@@H]1O[C@@H](O[C@H]2[C@H](Oc3cc(O)c4c(c3)O[C@H](c3ccc(O)cc3)CC4=O)O[C@H](CO)[C@@H](O)[C@@H]2O)[C@H](O)[C@H](O)[C@H]1O\tCHEMBL451532\n",
      "CC(=O)N1CCN(c2ccc(OCC3COC(Cn4ccnc4)(c4ccc(Cl)cc4Cl)O3)cc2)CC1\tCHEMBL157101\n",
      "O=C1C[C@@H](c2ccc(O)cc2)Oc2cc(O)ccc21\tCHEMBL252642\n",
      "O=c1c(O)c(-c2ccc(O)c(O)c2)oc2c(O)c(O)cc(O)c12\tCHEMBL253570\n",
      "O=c1c(O)c(-c2cc(O)c(O)c(O)c2)oc2cc(O)cc(O)c12\tCHEMBL164\n",
      "O=c1cc(-c2ccc(O)cc2)oc2cc(O)cc(O)c12\tCHEMBL28\n",
      "CC(C)(C)c1ccc(CN(Cc2cccc3ccccc23)n2ccnc2)cc1\tCHEMBL478647\n",
      "Clc1ccc(CC(Cc2ccccc2)n2ccnc2)s1\tCHEMBL518586\n",
      "Clc1ccc(CC(Cc2ccc(-c3ccccc3)cc2)n2ccnc2)s1\tCHEMBL475944\n",
      "Clc1ccc(CN(Cc2cccc3ccsc23)n2ccnc2)c(Cl)c1\tCHEMBL514514\n",
      "c1ccc(CN(Cc2cccc3ccsc23)n2ccnc2)cc1\tCHEMBL475943\n",
      "Clc1ccc(CN(Cc2ccc(Cl)s2)n2ccnc2)c(Cl)c1\tCHEMBL517347\n",
      "Clc1ccc(CN(Cc2ccccc2)n2ccnc2)s1\tCHEMBL476143\n",
      "Clc1ccc(CN(Cc2ccc(-c3ccccc3)cc2)n2ccnc2)s1\tCHEMBL476979\n",
      "N#Cc1ccc(C2CCCc3cncn32)cc1\tCHEMBL9298\n",
      "CC(C)(C)c1ccc(CN(Cc2cccs2)n2cncn2)cc1\tCHEMBL517816\n",
      "CC(C)(C)c1ccc(CN(CC/C=C/c2ccccc2)n2cncn2)cc1\tCHEMBL478024\n",
      "Clc1ccc(CN(CC/C=C/c2ccccc2)n2cncn2)c(Cl)c1\tCHEMBL478023\n",
      "C(=C/c1ccccc1)\\CCN(Cc1cccc2ccccc12)n1cncn1\tCHEMBL491010\n",
      "Fc1ccc(CC(Cc2ccc(-c3ccccc3)cc2)n2ccnc2)c(F)c1\tCHEMBL489360\n",
      "Clc1ccc(CC(Cc2ccc(-c3ccccc3)cc2)n2ccnc2)c(Cl)c1\tCHEMBL489359\n",
      "COc1ccc(CC(Cc2ccc(-c3ccccc3)cc2)n2ccnc2)cc1\tCHEMBL490584\n",
      "COc1ccccc1CC(Cc1ccc(-c2ccccc2)cc1)n1ccnc1\tCHEMBL521600\n",
      "Fc1ccc(CC(Cc2ccc(-c3ccccc3)cc2)n2ccnc2)cc1\tCHEMBL523813\n",
      "Cc1ccc(CC(Cc2ccc(-c3ccccc3)cc2)n2ccnc2)cc1\tCHEMBL522089\n",
      "Fc1ccccc1CC(Cc1ccc(-c2ccccc2)cc1)n1ccnc1\tCHEMBL489189\n",
      "Clc1ccccc1CC(Cc1ccc(-c2ccccc2)cc1)n1ccnc1\tCHEMBL489188\n",
      "Cc1ccccc1CC(Cc1ccc(-c2ccccc2)cc1)n1ccnc1\tCHEMBL489187\n",
      "c1ccc(CC(Cc2ccc(-c3ccccc3)cc2)n2ccnc2)cc1\tCHEMBL491222\n",
      "CC(C)(C)c1ccc(CC(Cc2ccc(-c3ccccc3)cc2)n2ccnc2)cc1\tCHEMBL491023\n",
      "CC(C)(C)c1ccc(CN(Cc2cccs2)n2ccnc2)cc1\tCHEMBL523973\n",
      "CC(C)(C)c1ccc(CN(Cc2ccc(Cl)cc2Cl)n2ccnc2)cc1\tCHEMBL491825\n",
      "Fc1ccc(CN(Cc2ccc(-c3ccccc3)cc2)n2ccnc2)c(F)c1\tCHEMBL491824\n",
      "Clc1ccc(CN(Cc2ccc(-c3ccccc3)cc2)n2ccnc2)c(Cl)c1\tCHEMBL490194\n",
      "COc1ccc(CN(Cc2ccc(-c3ccccc3)cc2)n2ccnc2)cc1\tCHEMBL491215\n",
      "COc1ccccc1CN(Cc1ccc(-c2ccccc2)cc1)n1ccnc1\tCHEMBL491214\n",
      "Fc1ccc(CN(Cc2ccc(-c3ccccc3)cc2)n2ccnc2)cc1\tCHEMBL491213\n",
      "Clc1ccc(CN(Cc2ccc(-c3ccccc3)cc2)n2ccnc2)cc1\tCHEMBL491013\n",
      "Cc1ccc(CN(Cc2ccc(-c3ccccc3)cc2)n2ccnc2)cc1\tCHEMBL490187\n",
      "Fc1ccccc1CN(Cc1ccc(-c2ccccc2)cc1)n1ccnc1\tCHEMBL489986\n",
      "Clc1ccccc1CN(Cc1ccc(-c2ccccc2)cc1)n1ccnc1\tCHEMBL521591\n",
      "Cc1ccccc1CN(Cc1ccc(-c2ccccc2)cc1)n1ccnc1\tCHEMBL490795\n",
      "c1ccc(CN(Cc2ccc(-c3ccccc3)cc2)n2ccnc2)cc1\tCHEMBL490794\n",
      "CC(C)(C)c1ccc(CN(Cc2ccc(-c3ccccc3)cc2)n2ccnc2)cc1\tCHEMBL516275\n",
      "Clc1ccc(CN(Cc2cccc3ccccc23)n2ccnc2)c(Cl)c1\tCHEMBL478648\n",
      "CCCCCCCC/C=C\\CCCCCCCC(=O)O\tCHEMBL8659\n",
      "CC/C=C\\C/C=C\\C/C=C\\C/C=C\\C/C=C\\C/C=C\\CCC(=O)O\tCHEMBL367149\n",
      "CC/C=C\\C/C=C\\C/C=C\\C/C=C\\C/C=C\\CCCCCC(=O)O\tCHEMBL496634\n",
      "CC/C=C\\C/C=C\\C/C=C\\C/C=C\\C/C=C\\CCCC(=O)O\tCHEMBL460026\n",
      "CCCCC/C=C\\C/C=C\\C/C=C\\C/C=C\\CCCC(=O)O\tCHEMBL15594\n",
      "CC/C=C\\C/C=C\\C/C=C\\CCCCCCCC(=O)O\tCHEMBL8739\n",
      "CCCCC/C=C\\C/C=C\\CCCCCCCC(=O)O\tCHEMBL267476\n",
      "N#Cc1ccc(C(c2ccc3cc[nH]c3c2)n2ccnc2)cc1\tCHEMBL469260\n",
      "CC(C)(C#N)c1cc(Cn2cncn2)cc(C(C)(C)C#N)c1\tCHEMBL1399\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL1444\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "Cl.N#Cc1ccc(C2CCCc3cncn32)cc1\tCHEMBL468419\n",
      "N#Cc1ccc(C(c2cccc3[nH]ccc23)n2cncn2)cc1\tCHEMBL513168\n",
      "N#Cc1ccc(C(c2cccc3[nH]ccc23)n2ccnc2)cc1\tCHEMBL468410\n",
      "N#Cc1ccc(C(c2ccc3cc[nH]c3c2)n2cncn2)cc1\tCHEMBL512994\n",
      "N#Cc1ccc(C(c2ccc3[nH]ccc3c2)n2ccnc2)cc1\tCHEMBL205177\n",
      "N#Cc1ccc([C@@H](c2ccc(OS(N)(=O)=O)c(Br)c2)n2cncn2)cc1\tCHEMBL480321\n",
      "NS(=O)(=O)Oc1ccc(C(c2ccc(OS(N)(=O)=O)cc2)n2cncn2)cc1\tCHEMBL479942\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL1444\n",
      "N#Cc1ccc([C@H](c2ccc(OS(N)(=O)=O)c(Br)c2)n2cncn2)cc1\tCHEMBL479559\n",
      "N#Cc1ccc(C(c2ccc(OS(N)(=O)=O)c(Br)c2)n2cncn2)cc1\tCHEMBL518761\n",
      "N#Cc1ccc(C(c2ccc(OS(N)(=O)=O)c(Cl)c2)n2cncn2)cc1\tCHEMBL479551\n",
      "N#Cc1ccc(C(c2ccc(OS(N)(=O)=O)cc2)n2cncn2)cc1\tCHEMBL479366\n",
      "NS(=O)(=O)Oc1ccc(C(c2ccc(OS(N)(=O)=O)c(Br)c2)n2cncn2)cc1Br\tCHEMBL479928\n",
      "NS(=O)(=O)Oc1cccc(C(c2cccc(OS(N)(=O)=O)c2)n2cncn2)c1\tCHEMBL479737\n",
      "NS(=O)(=O)Oc1ccc2c3c(c(=O)oc2c1)CCCCC3\tCHEMBL286738\n",
      "N#Cc1ccc([C@@H](c2ccc(O)c(Br)c2)n2cncn2)cc1\tCHEMBL500976\n",
      "N#Cc1ccc([C@H](c2ccc(O)c(Br)c2)n2cncn2)cc1\tCHEMBL453736\n",
      "N#Cc1ccc(C(c2ccc(O)c(Br)c2)n2cncn2)cc1\tCHEMBL481091\n",
      "N#Cc1ccc(C(c2ccc(O)c(Cl)c2)n2cncn2)cc1\tCHEMBL479385\n",
      "N#Cc1ccc(C(c2ccc(O)cc2)n2cncn2)cc1\tCHEMBL519937\n",
      "COc1cc(C(c2ccc(O)c(OC)c2)n2cncn2)ccc1O\tCHEMBL480516\n",
      "COc1ccc(C(c2ccc(OC)c(O)c2)n2cncn2)cc1O\tCHEMBL480515\n",
      "Oc1ccc(C(c2ccc(O)c(Br)c2)n2cncn2)cc1Br\tCHEMBL480451\n",
      "Oc1ccc(C(c2ccc(O)cc2)n2cncn2)cc1\tCHEMBL480514\n",
      "Oc1cccc(C(c2cccc(O)c2)n2cncn2)c1\tCHEMBL455832\n",
      "Cl.N#Cc1ccc([C@@H]2CCCc3cncn32)cc1\tCHEMBL481092\n",
      "Cn1nnc2ccc(C(c3ccc(Cl)cc3)n3cncn3)cc21\tCHEMBL100749\n",
      "Cn1nnc2ccc([C@H](c3ccc(Cl)cc3)n3cncn3)cc21\tCHEMBL224060\n",
      "Cn1nnc2ccc([C@@H](c3ccc(Cl)cc3)n3cncn3)cc21\tCHEMBL519436\n",
      "Cl.N#Cc1ccc([C@H]2CCCc3cncn32)cc1\tCHEMBL480718\n",
      "c1cncc(-c2ccc3ccccc3c2)c1\tCHEMBL365735\n",
      "COc1ccc2cc(-c3cccnc3)ccc2c1\tCHEMBL362968\n",
      "N#Cc1ccc2cc(-c3cccnc3)ccc2c1\tCHEMBL196796\n",
      "N#Cc1ccc(C2CCCc3cncn32)cc1\tCHEMBL9298\n",
      "O=[N+]([O-])c1ccc(Cn2ccnc2)cc1\tCHEMBL13784\n",
      "Fc1ccc(Cn2ccnc2)cc1\tCHEMBL377770\n",
      "Clc1ccc(Cn2ccnc2)cc1\tCHEMBL441367\n",
      "N#Cc1ccc(Cn2ccnc2)cc1\tCHEMBL349822\n",
      "Cc1ccc(Cn2ccnc2)cc1\tCHEMBL212741\n",
      "N#CC(C#N)=Cc1ccc(O)c(O)c1\tCHEMBL76904\n",
      "Clc1ccc(Cn2ccnc2)cc1\tCHEMBL441367\n",
      "Cc1cc(O)c2c(c1-c1ccc3c(c1O)C(=O)C[C@](C)([C@@H]1OC(=O)C[C@@H]1C)O3)O[C@](C)([C@@H]1OC(=O)C[C@H]1O)CC2=O\tCHEMBL586557\n",
      "Cc1cc2c(c(O)c1-c1ccc3c(c1O)C(=O)C[C@](C)([C@@H]1OC(=O)C[C@@H]1C)O3)C(=O)C[C@@](C)([C@@H]1OC(=O)C[C@H]1O)O2\tCHEMBL576579\n",
      "CC(=O)N1CCN(c2ccc(OCC3COC(Cn4ccnc4)(c4ccc(Cl)cc4Cl)O3)cc2)CC1\tCHEMBL157101\n",
      "C[C@H]1C2=CC(=O)CCC(C)(O2)[C@H]2CC[C@]3(C)C(=O)CC[C@H]3[C@@H]21\tCHEMBL574960\n",
      "Cc1nc2c(cc1C#N)C(Nc1ccc(Cl)cc1)Oc1ccc([N+](=O)[O-])cc1-2\tCHEMBL572637\n",
      "CC/C(=C1\\C=CC(=O)S1)C(CC)c1ccc(OC(C)=O)cc1\tCHEMBL583989\n",
      "CC1C(O)CC23COC(=O)C2=CCCC3C1(C)CCC1=CC(=O)OC1\tCHEMBL582837\n",
      "CCCC(=O)Oc1cc(OC(=O)CCC)c2c(C)cc(=O)oc2c1\tCHEMBL583310\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=C(O)C(=O)CC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL132530\n",
      "CC1C2=CC(=O)CCC2(C)C2CCC3(C)C(=O)CCC3C12\tCHEMBL583076\n",
      "CC(C)(C#N)c1cc(Cn2cncn2)cc(-c2ccc(OS(N)(=O)=O)c(Cl)c2)c1\tCHEMBL604716\n",
      "N#Cc1ccc(Cn2cncn2)cc1-c1ccc(Cl)c(OS(N)(=O)=O)c1\tCHEMBL599239\n",
      "CN(C)S(=O)(=O)Oc1ccc(-c2cc(Cn3cncn3)ccc2C#N)cc1\tCHEMBL596779\n",
      "N#Cc1ccc(Cn2cncn2)cc1-c1ccc(OS(N)(=O)=O)c(Cl)c1\tCHEMBL592131\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL1444\n",
      "CC(C)(C#N)c1cc(Cn2cncn2)cc(C(C)(C)C#N)c1\tCHEMBL1399\n",
      "N#Cc1ccc(Cn2cncn2)c(-c2ccc(OS(N)(=O)=O)c(Cl)c2)c1\tCHEMBL598212\n",
      "N#Cc1ccc(Cn2cncn2)cc1-c1ccc(OS(N)(=O)=O)cc1\tCHEMBL598199\n",
      "CN(C)S(=O)(=O)Oc1ccc(-c2cc(Cn3cncn3)ccc2C#N)cc1Cl\tCHEMBL599238\n",
      "CC(C)(C#N)c1cc(Cn2cncn2)cc(-c2ccc(OS(N)(=O)=O)cc2)c1\tCHEMBL597403\n",
      "NS(=O)(=O)Oc1ccc(-c2cccc(Cn3cncn3)c2)cc1Cl\tCHEMBL598009\n",
      "N#Cc1cc(Cn2cncn2)cc(-c2ccc(OS(N)(=O)=O)c(Cl)c2)c1\tCHEMBL598628\n",
      "N#Cc1ccc(-c2ccc(OS(N)(=O)=O)c(Cl)c2)cc1Cn1cncn1\tCHEMBL599442\n",
      "N#Cc1ccc(-c2ccc(OS(N)(=O)=O)cc2)cc1Cn1cncn1\tCHEMBL592376\n",
      "NS(=O)(=O)Oc1ccc(-c2cccc(Cn3cncn3)c2)cc1\tCHEMBL598629\n",
      "N#Cc1cc(Cn2cncn2)cc(-c2ccc(OS(N)(=O)=O)cc2)c1\tCHEMBL598627\n",
      "N#Cc1cc(Cn2cncn2)ccc1-c1ccc(OS(N)(=O)=O)cc1\tCHEMBL609618\n",
      "NS(=O)(=O)Oc1ccc2c3c(c(=O)oc2c1)CCCCC3\tCHEMBL286738\n",
      "N#Cc1cc(Cn2cncn2)ccc1-c1ccc(OS(N)(=O)=O)c(Cl)c1\tCHEMBL598211\n",
      "N#Cc1ccc(Cn2cncn2)cc1-c1ccccc1\tCHEMBL592852\n",
      "N#Cc1ccc(Cn2cncn2)cc1-c1ccc(O)cc1\tCHEMBL597606\n",
      "N#Cc1ccc(Cn2cncn2)cc1-c1ccc(O)c(Cl)c1\tCHEMBL597808\n",
      "N#Cc1ccc(Cn2cncn2)cc1-c1ccc(Cl)c(O)c1\tCHEMBL599864\n",
      "N#Cc1ccc(-c2cccc(O)c2)cc1Cn1cncn1\tCHEMBL589247\n",
      "N#Cc1ccc(-c2ccc(O)c(Cl)c2)cc1Cn1cncn1\tCHEMBL609332\n",
      "N#Cc1cc(Cn2cncn2)cc(-c2ccccc2)c1\tCHEMBL599455\n",
      "N#Cc1cc(Cn2cncn2)cc(-c2ccc(F)cc2)c1\tCHEMBL599456\n",
      "N#Cc1ccc(-c2cc(C#N)cc(Cn3cncn3)c2)cc1\tCHEMBL609333\n",
      "N#Cc1cc(Cn2cncn2)cc(-c2ccc(O)cc2)c1\tCHEMBL601840\n",
      "N#Cc1cc(Cn2cncn2)cc(-c2ccc(O)c(Cl)c2)c1\tCHEMBL598846\n",
      "c1ccc(-c2cccc(Cn3cncn3)c2)cc1\tCHEMBL598847\n",
      "N#Cc1cc(Cn2cncn2)ccc1-c1ccccc1\tCHEMBL608153\n",
      "N#Cc1cc(Cn2cncn2)ccc1-c1ccc(O)cc1\tCHEMBL607855\n",
      "N#Cc1cc(Cn2cncn2)ccc1-c1ccc(O)c(Cl)c1\tCHEMBL598020\n",
      "N#Cc1ccc(Cn2cncn2)c(-c2ccccc2)c1\tCHEMBL589738\n",
      "N#Cc1ccc(Cn2cncn2)c(-c2ccc(O)c(Cl)c2)c1\tCHEMBL598223\n",
      "CC(C)(C#N)c1cc(Cn2cncn2)cc(-c2ccccc2)c1\tCHEMBL598224\n",
      "CC(C)(C#N)c1cc(Cn2cncn2)cc(-c2ccc(O)cc2)c1\tCHEMBL605418\n",
      "CC(C)(C#N)c1cc(Cn2cncn2)cc(-c2ccc(O)c(Cl)c2)c1\tCHEMBL597416\n",
      "N#Cc1ccc(Cn2cncn2)cc1-c1cccc(Cl)c1\tCHEMBL589974\n",
      "O=C(OC[C@H](O)[C@H](O)COC(=O)/C(Cc1ccc(O)cc1)=N\\O)/C(Cc1ccc(O)cc1)=N\\O\tCHEMBL1088570\n",
      "Cn1nnc2ccc([C@H](c3ccc(Cl)cc3)n3cncn3)cc21\tCHEMBL224060\n",
      "O=c1c(C(c2ccc([N+](=O)[O-])cc2)n2ccnc2)coc2ccccc12\tCHEMBL516643\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2ccnc2)cc1\tCHEMBL29482\n",
      "N#Cc1ccc(C2CCCc3cncn32)cc1\tCHEMBL9298\n",
      "Cn1c(=O)sc2cc(C(c3ccccc3)n3ccnc3)ccc21\tCHEMBL1097041\n",
      "CC(C)(C#N)c1cc(Cn2cncn2)cc(C(C)(C)C#N)c1\tCHEMBL1399\n",
      "O=c1cc(-c2ccccc2)oc2c1ccc1cccc(O)c12\tCHEMBL1097042\n",
      "CCn1c(C)c(C(c2ccc(F)cc2)n2ccnc2)c2ccccc21\tCHEMBL1097043\n",
      "O=C1/C(=C\\c2cccnc2)C(c2ccc(OC(F)(F)F)cc2)n2cccc21\tCHEMBL1095413\n",
      "COc1ccc2c(c1)C1C(CC2)C1c1ccncc1\tCHEMBL294361\n",
      "Fc1ccc(-n2cc(Cn3ccnc3)c3ccccc32)cc1\tCHEMBL1094426\n",
      "c1ccc(C2(n3ccnc3)c3ccccc3-c3ccccc32)cc1\tCHEMBL223874\n",
      "O=c1c(-c2ccccc2)c(SCc2ccncc2)oc2cc(OCc3ccc(-c4ccccc4)cc3)ccc12\tCHEMBL1094427\n",
      "CCn1cc(C(c2ccccc2)n2ccnc2)c2cc(Br)ccc21\tCHEMBL1094428\n",
      "O=C1/C(=C\\c2cccnc2)C(c2ccc(Cl)cc2)Cn2cccc21\tCHEMBL1098737\n",
      "Fc1ccc2ccc(Cn3ccnc3)cc2c1\tCHEMBL1098424\n",
      "O=[N+]([O-])c1ccc(CCn2ccnc2)cc1\tCHEMBL1098425\n",
      "c1cnc2c(c1)C(Cn1ccnc1)CCC2\tCHEMBL300109\n",
      "CC(C)/C=C/Cc1c(O)cc(O)c2c1OC(c1ccc(O)cc1)CC2=O\tCHEMBL1098426\n",
      "Clc1cccc(C(c2ccc3nc[nH]c3c2)n2ccnc2)c1\tCHEMBL389433\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL1444\n",
      "O=c1cc(-c2ccccc2)oc2c1ccc1ccccc12\tCHEMBL283196\n",
      "O=C1c2cccc(O)c2CCC1Cc1ccncc1\tCHEMBL93828\n",
      "O=C1c2ccc(Oc3ccccc3)cc2OC(SCc2cccnc2)C1c1ccccc1\tCHEMBL1096744\n",
      "Fc1ccc(CCCn2ccnc2)cc1\tCHEMBL214932\n",
      "Clc1ccc(COC(Cn2ccnc2)c2ccc(Cl)cc2Cl)c(Cl)c1\tCHEMBL91\n",
      "O=c1cc(-c2ccc(O)cc2)oc2cc(O)cc(O)c12\tCHEMBL28\n",
      "COc1ccc(C2=C(c3ccc(OC)c(OC)c3)C(=O)OC2)cc1OC\tCHEMBL1098382\n",
      "O=c1cc(-c2ccccc2)oc2cc(O)cc(O)c12\tCHEMBL117\n",
      "Clc1ccccc1C(c1ccccc1)(c1ccccc1)n1ccnc1\tCHEMBL104\n",
      "CC1=CC[C@@H]2[C@H]1[C@H]1OC(=O)[C@@H](C)[C@@H]1CC[C@]2(C)O\tCHEMBL1097371\n",
      "O=c1cc(-c2ccc(O)cc2)oc2cc(O)ccc12\tCHEMBL294878\n",
      "O=C1C[C@@H](c2ccc(O)cc2)Oc2cc(O)cc(O)c21\tCHEMBL9352\n",
      "COc1ccc2c(c1)C(N)=C(CC1=CCNC=C1)C2=O\tCHEMBL1096024\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "COc1ccc(C2=C(c3ccc(OC)c(OC)c3)C(=O)OC2)cc1\tCHEMBL1096439\n",
      "O=c1c(O)c(-c2ccc(O)c(O)c2)oc2cc(O)cc(O)c12\tCHEMBL50\n",
      "O=c1cc(-c2ccccc2)oc2ccccc12\tCHEMBL275638\n",
      "O=c1cc(-c2ccccc2)oc2cc(O)c(O)cc12\tCHEMBL300814\n",
      "Clc1ccc(C(c2cc3ccccc3o2)n2ccnc2)cc1\tCHEMBL1097980\n",
      "C=C1C(=O)O[C@H]2[C@H]1CCC(C)=C1C(=O)C=C(C)[C@@H]12\tCHEMBL441260\n",
      "Cc1cccc(O)c1C(=O)c1c(O)cc(C(=O)O)cc1C=O\tCHEMBL1097763\n",
      "C=C1C(=O)O[C@H]2[C@H]1CCC(C)=C1C[C@H]3O[C@@]3(C)[C@@H]12\tCHEMBL1097764\n",
      "CC(=O)N1CCN(c2ccc(OCC3COC(Cn4ccnc4)(c4ccc(Cl)cc4Cl)O3)cc2)CC1\tCHEMBL157101\n",
      "C=C1C(=O)O[C@H]2[C@H]1CCC[C@@H]1CC=C(C)[C@H]21\tCHEMBL1099308\n",
      "COc1ccc(-c2coc3cc(O)cc(O)c3c2=O)cc1\tCHEMBL131921\n",
      "COc1ccc(C2=C(c3cc(OC)c(OC)c(OC)c3)COC2=O)cc1OC\tCHEMBL1094331\n",
      "COc1cc(C(=O)N(C)C)cc(C=O)c1C(=O)c1c(C)cccc1O\tCHEMBL1094149\n",
      "O=c1cc(-c2ccccc2)oc2cc(O)ccc12\tCHEMBL276915\n",
      "O=C1OC[C@H](Cc2ccc(O)c(O)c2)[C@H]1Cc1ccc(O)c(O)c1\tCHEMBL369471\n",
      "O=C1OC[C@H](Cc2ccccc2)[C@H]1Cc1cccc(O)c1\tCHEMBL1094154\n",
      "O=C1/C(=C\\c2ccncc2)C(c2ccc(Cl)cc2)Cn2cccc21\tCHEMBL9165\n",
      "CC(Cc1ccc(O)c(O)c1)C(C)Cc1ccc(O)c(O)c1\tCHEMBL52\n",
      "O=c1c2ccccc2sc2c(Cn3ccnc3)cccc12\tCHEMBL1083353\n",
      "O=[N+]([O-])c1ccc(Cn2ccnc2)c(Sc2ccccc2)c1\tCHEMBL1085936\n",
      "O=[N+]([O-])c1ccc(Cn2ccnc2)c(Oc2ccccc2)c1\tCHEMBL1083653\n",
      "O=c1c2ccccc2sc2c(Cn3ccnc3)ccc([N+](=O)[O-])c12\tCHEMBL1083665\n",
      "O=c1c2ccccc2oc2c(Cn3ccnc3)cccc12\tCHEMBL1083971\n",
      "O=c1c2ccccc2oc2c(Cn3ccnc3)ccc([N+](=O)[O-])c12\tCHEMBL159073\n",
      "N#Cc1ccc(C2CCCc3cncn32)cc1\tCHEMBL9298\n",
      "O=c1c2ccccc2oc2c(Cn3ccnc3)cc([N+](=O)[O-])cc12\tCHEMBL1083663\n",
      "O=c1c2cccnc2oc2c(Cn3ccnc3)ccc([N+](=O)[O-])c12\tCHEMBL1083970\n",
      "O=c1c2ccccc2oc2cccc(Cn3ccnc3)c12\tCHEMBL1083652\n",
      "c1ccc(Oc2ccccc2Cn2ccnc2)cc1\tCHEMBL1082760\n",
      "O=c1c2ccccc2oc2cc(Cn3ccnc3)ccc12\tCHEMBL350922\n",
      "O=c1c2ccccc2oc2c(Cn3ccnc3)c([N+](=O)[O-])ccc12\tCHEMBL1083664\n",
      "O=c1c2ccccc2oc2ccc(Cn3ccnc3)cc12\tCHEMBL1083354\n",
      "COc1cc(/C=C/c2ccc(N)cc2)cc(OC)c1\tCHEMBL324290\n",
      "COc1ccc(/C=C/c2ccc(N)cc2)c(OC)c1\tCHEMBL1170662\n",
      "COc1ccc(/C=C/c2cccc(N)c2)c(OC)c1\tCHEMBL1173641\n",
      "COc1ccc(OC)c(/C=C/c2ccc(N)cc2)c1\tCHEMBL1173699\n",
      "COc1cccc(/C=C/c2ccc(N)cc2)c1OC\tCHEMBL1173700\n",
      "COc1ccc(/C=C/c2ccc(N)cc2)cc1OC\tCHEMBL1172937\n",
      "COc1cc(/C=C/c2cccc(N)c2)cc(OC)c1OC\tCHEMBL1173065\n",
      "COc1cc(/C=C/c2ccc(N)cc2)cc(OC)c1OC\tCHEMBL87477\n",
      "COc1ccc(/C=C/c2ccc(N)cc2)c(OC)c1OC\tCHEMBL1173067\n",
      "COc1cc(/C=C/c2ccc(N)cc2)c(OC)c(OC)c1\tCHEMBL1170663\n",
      "Nc1cccc(/C=C/c2ccc3c(c2)OCO3)c1\tCHEMBL1173129\n",
      "Nc1ccc(/C=C/c2ccc3c(c2)OCO3)cc1\tCHEMBL1173130\n",
      "O=[N+]([O-])c1ccc(/C=C/c2cccc([N+](=O)[O-])c2)cc1\tCHEMBL1173131\n",
      "COc1cccc(/C=C/c2ccc(N)cc2)c1\tCHEMBL1173573\n",
      "COc1cc(CCc2ccc(N)cc2)cc(OC)c1\tCHEMBL1171818\n",
      "COc1ccc(CCc2ccc(N)cc2)cc1OC\tCHEMBL1171680\n",
      "COc1cc(CCc2ccc(N)cc2)cc(OC)c1OC\tCHEMBL86928\n",
      "Nc1ccc(CCc2cccc(N)c2)cc1\tCHEMBL1172396\n",
      "Nc1ccc(/C=C/c2cccc(N)c2)cc1\tCHEMBL1171628\n",
      "Nc1ccc(/C=C/c2cc(N)cc(N)c2)cc1\tCHEMBL1171091\n",
      "CC(=O)Oc1cc(/C=C/c2ccc(N)cc2)cc(OC(C)=O)c1\tCHEMBL1172400\n",
      "Nc1ccc(/C=C/c2cc(O)cc(O)c2)cc1\tCHEMBL1172402\n",
      "O=C(Cn1ccnc1)c1ccc([N+](=O)[O-])cc1\tCHEMBL162549\n",
      "Nc1ccc(C(=O)Cn2ccnc2)cc1\tCHEMBL162431\n",
      "COc1cc(/C=C(\\Cn2ccnc2)c2ccc([N+](=O)[O-])cc2)cc(OC)c1\tCHEMBL1170678\n",
      "COc1cc(/C=C(\\Cn2ccnc2)c2ccc(N)cc2)cc(OC)c1\tCHEMBL1170893\n",
      "Oc1cccc(-c2ccc(Cc3ccncc3)cc2)c1\tCHEMBL1172882\n",
      "CC(C)=C(c1ccncc1)c1ccc(-c2cccc(O)c2)cc1\tCHEMBL1173259\n",
      "Oc1ccc(-c2ccc(Cc3ccncc3)cc2)cc1F\tCHEMBL1173260\n",
      "CC(C)=C(c1ccncc1)c1ccc(-c2ccc(O)c(F)c2)cc1\tCHEMBL1173335\n",
      "Oc1ccc(-c2ccc(Cc3ccncc3)cc2)cc1O\tCHEMBL1172779\n",
      "CC(C)=C(c1ccncc1)c1ccc(-c2ccc(O)c(O)c2)cc1\tCHEMBL1172847\n",
      "Nc1cccc(-c2ccc(Cc3ccncc3)cc2)c1\tCHEMBL1171987\n",
      "CC(C)=C(c1ccncc1)c1ccc(-c2cccc(N)c2)cc1\tCHEMBL1173326\n",
      "CC(C)=C(c1ccncc1)c1ccc(-c2ccc(N)c(N)c2)cc1\tCHEMBL1171991\n",
      "CC(C)=C(c1ccncc1)c1ccc(-c2ccc3cc(O)ccc3c2)cc1\tCHEMBL1172842\n",
      "CC(=O)N1CCN(c2ccc(OCC3COC(Cn4ccnc4)(c4ccc(Cl)cc4Cl)O3)cc2)CC1\tCHEMBL157101\n",
      "C[C@]12CC[C@H]3[C@@H](CC=C4C[C@@H](O)CC[C@@]43C)[C@@H]1CC=C2c1cccnc1\tCHEMBL254328\n",
      "Nc1ccc(-c2ccc(Cc3ccncc3)cc2)cc1N\tCHEMBL1173327\n",
      "Oc1ccc(-c2ccc(C(O)c3ccncc3)cc2)cc1\tCHEMBL1215664\n",
      "Nc1ccc(-c2ccc(Cc3ccncc3)cc2)cc1\tCHEMBL1215665\n",
      "Nc1cccc(-c2ccc(Cc3ccncc3)cc2)c1\tCHEMBL1171987\n",
      "Nc1ccc(-c2ccc(Cc3ccncc3)cc2)cc1N\tCHEMBL1173327\n",
      "Oc1cccc(-c2ccc(Cc3ccncc3)cc2)c1\tCHEMBL1172882\n",
      "Oc1ccc(-c2ccc(Cc3ccncc3)cc2)cc1O\tCHEMBL1172779\n",
      "Oc1ccc(-c2ccc(Cc3ccncc3)cc2)cc1F\tCHEMBL1173260\n",
      "C(=C1\\CCc2ccccc21)\\c1cccnc1\tCHEMBL364080\n",
      "C(=C1/CCc2ccccc21)\\c1cccnc1\tCHEMBL363621\n",
      "C(=C1\\CCCc2ccccc21)\\c1cccnc1\tCHEMBL193606\n",
      "C(=C1/CCCc2ccccc21)\\c1cccnc1\tCHEMBL365120\n",
      "Fc1ccc2c(c1)CC/C2=C\\c1cccnc1\tCHEMBL193997\n",
      "Fc1ccc2c(c1)CC/C2=C/c1cccnc1\tCHEMBL383806\n",
      "Clc1ccc2c(c1)CC/C2=C\\c1cccnc1\tCHEMBL383807\n",
      "Clc1ccc2c(c1)CC/C2=C/c1cccnc1\tCHEMBL193652\n",
      "Brc1ccc2c(c1)CC/C2=C\\c1cccnc1\tCHEMBL194440\n",
      "Brc1ccc2c(c1)CC/C2=C/c1cccnc1\tCHEMBL176118\n",
      "COc1ccc2c(c1)CC/C2=C\\c1cccnc1\tCHEMBL176609\n",
      "COc1ccc2c(c1)CC/C2=C/c1cccnc1\tCHEMBL176101\n",
      "COc1ccc2c(c1)CCC/C2=C\\c1cccnc1\tCHEMBL362441\n",
      "COc1ccc2c(c1)CCC/C2=C/c1cccnc1\tCHEMBL176098\n",
      "COc1cc2c(cc1OC)/C(=C/c1cccnc1)CCC2\tCHEMBL366669\n",
      "CCOc1ccc2c(c1)CC/C2=C\\c1cccnc1\tCHEMBL425323\n",
      "CCOc1ccc2c(c1)CC/C2=C/c1cccnc1\tCHEMBL176886\n",
      "Cc1ccc2c(c1)/C(=C/c1cccnc1)CC2\tCHEMBL175550\n",
      "Cc1ccc2c(c1)/C(=C\\c1cccnc1)CC2\tCHEMBL366570\n",
      "Cc1cccc2c1CC/C2=C\\c1cccnc1\tCHEMBL368305\n",
      "Cc1cccc2c1CC/C2=C/c1cccnc1\tCHEMBL176009\n",
      "Fc1cccc2c1CC/C2=C\\c1cccnc1\tCHEMBL366490\n",
      "Fc1cccc2c1CC/C2=C/c1cccnc1\tCHEMBL176076\n",
      "Clc1cccc2c1CC/C2=C\\c1cccnc1\tCHEMBL176287\n",
      "Clc1cccc2c1CC/C2=C/c1cccnc1\tCHEMBL369623\n",
      "C(=C1\\CCc2ccccc21)\\c1ccncc1\tCHEMBL175962\n",
      "C(=C1/CCc2ccccc21)\\c1ccncc1\tCHEMBL179149\n",
      "C(=C1\\CCCc2ccccc21)\\c1ccncc1\tCHEMBL360026\n",
      "Fc1ccc2c(c1)CC/C2=C\\c1ccncc1\tCHEMBL424960\n",
      "Fc1ccc2c(c1)CC/C2=C/c1ccncc1\tCHEMBL179144\n",
      "Clc1ccc2c(c1)CC/C2=C\\c1ccncc1\tCHEMBL175554\n",
      "Clc1ccc2c(c1)CC/C2=C/c1ccncc1\tCHEMBL175799\n",
      "Brc1ccc2c(c1)CC/C2=C\\c1ccncc1\tCHEMBL366773\n",
      "Brc1ccc2c(c1)CC/C2=C/c1ccncc1\tCHEMBL179222\n",
      "COc1ccc2c(c1)CC/C2=C/c1ccncc1\tCHEMBL175609\n",
      "COc1ccc2c(c1)CCC/C2=C\\c1ccncc1\tCHEMBL175979\n",
      "COc1ccc2c(c1)CCC/C2=C/c1ccncc1\tCHEMBL193496\n",
      "COc1ccc2c(c1)/C(=C/c1ccncc1)CC2\tCHEMBL365314\n",
      "COc1ccc2c(c1)/C(=C\\c1ccncc1)CC2\tCHEMBL370281\n",
      "COc1cc2c(cc1OC)/C(=C/c1ccncc1)CCC2\tCHEMBL193858\n",
      "Cc1ccc2c(c1)/C(=C/c1ccncc1)CC2\tCHEMBL371742\n",
      "Cc1ccc2c(c1)/C(=C\\c1ccncc1)CC2\tCHEMBL192077\n",
      "COc1ccc2c(c1)CC/C2=C\\c1cncs1\tCHEMBL192155\n",
      "COc1ccc2c(c1)CC/C2=C/c1cncs1\tCHEMBL195354\n",
      "Fc1ccc2c(c1)CC/C2=C\\c1cncnc1\tCHEMBL195248\n",
      "Fc1ccc2c(c1)CC/C2=C\\c1cccc2cnccc12\tCHEMBL370530\n",
      "Fc1ccc2c(c1)CC/C2=C/c1cccc2cnccc12\tCHEMBL363849\n",
      "C(=C1c2ccccc2-c2ccccc21)c1cccnc1\tCHEMBL194928\n",
      "C(=C1c2ccccc2-c2ccccc21)c1ccncc1\tCHEMBL383145\n",
      "C(=C(c1ccccc1)c1ccccc1)c1cccnc1\tCHEMBL370317\n",
      "CC1C/C(=C/c2cccnc2)c2ccccc21\tCHEMBL373074\n",
      "C(=C1\\CC(c2ccccc2)c2ccccc21)\\c1cccnc1\tCHEMBL193555\n",
      "C(=C1\\CCc2ccccc21)\\c1cccnc1\tCHEMBL364080\n",
      "C(=C1/CCc2ccccc21)\\c1cccnc1\tCHEMBL363621\n",
      "C(=C1\\CCCc2ccccc21)\\c1cccnc1\tCHEMBL193606\n",
      "C(=C1/CCCc2ccccc21)\\c1cccnc1\tCHEMBL365120\n",
      "Fc1ccc2c(c1)CC/C2=C\\c1cccnc1\tCHEMBL193997\n",
      "Fc1ccc2c(c1)CC/C2=C/c1cccnc1\tCHEMBL383806\n",
      "Clc1ccc2c(c1)CC/C2=C\\c1cccnc1\tCHEMBL383807\n",
      "Clc1ccc2c(c1)CC/C2=C/c1cccnc1\tCHEMBL193652\n",
      "Brc1ccc2c(c1)CC/C2=C\\c1cccnc1\tCHEMBL194440\n",
      "Brc1ccc2c(c1)CC/C2=C/c1cccnc1\tCHEMBL176118\n",
      "COc1ccc2c(c1)CC/C2=C\\c1cccnc1\tCHEMBL176609\n",
      "COc1ccc2c(c1)CC/C2=C/c1cccnc1\tCHEMBL176101\n",
      "COc1ccc2c(c1)CCC/C2=C\\c1cccnc1\tCHEMBL362441\n",
      "COc1ccc2c(c1)CCC/C2=C/c1cccnc1\tCHEMBL176098\n",
      "COc1cc2c(cc1OC)/C(=C/c1cccnc1)CCC2\tCHEMBL366669\n",
      "CCOc1ccc2c(c1)CC/C2=C\\c1cccnc1\tCHEMBL425323\n",
      "CCOc1ccc2c(c1)CC/C2=C/c1cccnc1\tCHEMBL176886\n",
      "Cc1ccc2c(c1)/C(=C/c1cccnc1)CC2\tCHEMBL175550\n",
      "Cc1ccc2c(c1)/C(=C\\c1cccnc1)CC2\tCHEMBL366570\n",
      "Cc1cccc2c1CC/C2=C\\c1cccnc1\tCHEMBL368305\n",
      "Cc1cccc2c1CC/C2=C/c1cccnc1\tCHEMBL176009\n",
      "Fc1cccc2c1CC/C2=C\\c1cccnc1\tCHEMBL366490\n",
      "Fc1cccc2c1CC/C2=C/c1cccnc1\tCHEMBL176076\n",
      "Clc1cccc2c1CC/C2=C\\c1cccnc1\tCHEMBL176287\n",
      "Clc1cccc2c1CC/C2=C/c1cccnc1\tCHEMBL369623\n",
      "C(=C1\\CCc2ccccc21)\\c1ccncc1\tCHEMBL175962\n",
      "C(=C1/CCc2ccccc21)\\c1ccncc1\tCHEMBL179149\n",
      "C(=C1\\CCCc2ccccc21)\\c1ccncc1\tCHEMBL360026\n",
      "Fc1ccc2c(c1)CC/C2=C\\c1ccncc1\tCHEMBL424960\n",
      "Fc1ccc2c(c1)CC/C2=C/c1ccncc1\tCHEMBL179144\n",
      "Clc1ccc2c(c1)CC/C2=C\\c1ccncc1\tCHEMBL175554\n",
      "Clc1ccc2c(c1)CC/C2=C/c1ccncc1\tCHEMBL175799\n",
      "Brc1ccc2c(c1)CC/C2=C\\c1ccncc1\tCHEMBL366773\n",
      "Brc1ccc2c(c1)CC/C2=C/c1ccncc1\tCHEMBL179222\n",
      "COc1ccc2c(c1)CC/C2=C/c1ccncc1\tCHEMBL175609\n",
      "COc1ccc2c(c1)CCC/C2=C\\c1ccncc1\tCHEMBL175979\n",
      "COc1ccc2c(c1)CCC/C2=C/c1ccncc1\tCHEMBL193496\n",
      "COc1ccc2c(c1)/C(=C/c1ccncc1)CC2\tCHEMBL365314\n",
      "COc1ccc2c(c1)/C(=C\\c1ccncc1)CC2\tCHEMBL370281\n",
      "COc1cc2c(cc1OC)/C(=C/c1ccncc1)CCC2\tCHEMBL193858\n",
      "Cc1ccc2c(c1)/C(=C/c1ccncc1)CC2\tCHEMBL371742\n",
      "Cc1ccc2c(c1)/C(=C\\c1ccncc1)CC2\tCHEMBL192077\n",
      "COc1ccc2c(c1)CC/C2=C\\c1cncs1\tCHEMBL192155\n",
      "COc1ccc2c(c1)CC/C2=C/c1cncs1\tCHEMBL195354\n",
      "Fc1ccc2c(c1)CC/C2=C\\c1cncnc1\tCHEMBL195248\n",
      "Fc1ccc2c(c1)CC/C2=C\\c1cccc2cnccc12\tCHEMBL370530\n",
      "Fc1ccc2c(c1)CC/C2=C/c1cccc2cnccc12\tCHEMBL363849\n",
      "C(=C1c2ccccc2-c2ccccc21)c1cccnc1\tCHEMBL194928\n",
      "C(=C1c2ccccc2-c2ccccc21)c1ccncc1\tCHEMBL383145\n",
      "C(=C(c1ccccc1)c1ccccc1)c1cccnc1\tCHEMBL370317\n",
      "CC1C/C(=C/c2cccnc2)c2ccccc21\tCHEMBL373074\n",
      "C(=C1\\CC(c2ccccc2)c2ccccc21)\\c1cccnc1\tCHEMBL193555\n",
      "C[C@]12CC[C@H]3[C@@H](CC[C@H]4C=CCC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL1629800\n",
      "C[C@]12CC[C@H]3[C@@H](CC[C@H]4C=CCC[C@@]43C)[C@@H]1CC[C@@H]2O\tCHEMBL1629801\n",
      "C[C@]12CC[C@H]3[C@@H](CC[C@H]4[C@@H]5O[C@@H]5CC[C@]34C)[C@@H]1CCC2=O\tCHEMBL1629802\n",
      "C[C@]12CC[C@H]3[C@@H](CC[C@H]4[C@@H]5O[C@@H]5CC[C@]34C)[C@@H]1CC[C@@H]2O\tCHEMBL1629803\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=C(O)C(=O)CC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL132530\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=CCCC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL1629804\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=CCCC[C@@]43CO)[C@@H]1CCC2=O\tCHEMBL1629805\n",
      "C[C@]12CC[C@H]3[C@@H](C[C@H](O)C4=CCCC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL1629806\n",
      "C[C@]12CC[C@H]3[C@@H](C[C@@H](O)C4=CCCC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL1630261\n",
      "CCCC(=O)OC1=C2CC[C@H]3[C@@H]4CCC(=O)[C@@]4(C)CC[C@@H]3[C@@]2(C)CCC1\tCHEMBL1630260\n",
      "CCCCCC(=O)OC1=C2CC[C@H]3[C@@H]4CCC(=O)[C@@]4(C)CC[C@@H]3[C@@]2(C)CCC1\tCHEMBL1630262\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=C(OC(=O)c5ccccc5)CCC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL1630263\n",
      "CC(=O)OC1=C2CC[C@H]3[C@@H]4CCC(=O)[C@@]4(C)CC[C@@H]3[C@@]2(C)CCC1\tCHEMBL1630264\n",
      "CC(=O)OC1=C2CC[C@H]3[C@@H]4CCC(=O)[C@@]4(C)CC[C@@H]3[C@@]2(CO)CCC1\tCHEMBL1630265\n",
      "C[C@]12CC[C@H]3[C@@H](C[C@H](O)C4=CCCC[C@@]43CO)[C@@H]1CCC2=O\tCHEMBL1630266\n",
      "C[C@]12CC[C@H]3[C@@H](C[C@@H](O)C4=CCCC[C@@]43CO)[C@@H]1CCC2=O\tCHEMBL1630267\n",
      "C[C@]12CC[C@H]3[C@@H](CC=C4[C@@H](O)CCC[C@]43C)[C@@H]1CCC2=O\tCHEMBL1630268\n",
      "CC(=O)OC1CCC[C@]2(C)C1=CC[C@H]1[C@@H]3CCC(=O)[C@@]3(C)CC[C@@H]12\tCHEMBL1630269\n",
      "CC(=O)OC1CCC[C@@]2(C)C1=CC[C@H]1[C@@H]3CCC(=O)[C@@]3(C)CC[C@@H]12\tCHEMBL1630270\n",
      "CC(=O)OC1CCC[C@@]2(CO)C1=CC[C@H]1[C@@H]3CCC(=O)[C@@]3(C)CC[C@@H]12\tCHEMBL1630271\n",
      "C[C@]12CC[C@H]3[C@@H](CC=C4[C@H](O)CCC[C@]43C)[C@@H]1CCC2=O\tCHEMBL1630272\n",
      "C[C@]12CC[C@H]3[C@@H](CC=C4[C@H](O)CCC[C@@]43CO)[C@@H]1CCC2=O\tCHEMBL1630273\n",
      "C[C@]12CC[C@H]3[C@@H](CC=C4[C@@H](O)CCC[C@@]43CO)[C@@H]1CCC2=O\tCHEMBL1630274\n",
      "CO[C@H]1CCC[C@@]2(CO)C1=CC[C@H]1[C@@H]3CCC(=O)[C@@]3(C)CC[C@@H]12\tCHEMBL1630275\n",
      "C[C@H]1CCC[C@@]2(C)C1=CC[C@H]1[C@@H]3CCC(=O)[C@@]3(C)CC[C@@H]12\tCHEMBL1630276\n",
      "CC[C@H]1CCC[C@@]2(C)C1=CC[C@H]1[C@@H]3CCC(=O)[C@@]3(C)CC[C@@H]12\tCHEMBL1630277\n",
      "CCC[C@H]1CCC[C@@]2(C)C1=CC[C@H]1[C@@H]3CCC(=O)[C@@]3(C)CC[C@@H]12\tCHEMBL1630278\n",
      "C[C@]12CC[C@H]3[C@@H](CC[C@H]4C(=O)CCC[C@]34C)[C@@H]1CCC2=O\tCHEMBL1630279\n",
      "C[C@]12CC[C@H]3[C@@H](CC[C@H]4C(=O)[C@H](O)CC[C@]34C)[C@@H]1CCC2=O\tCHEMBL1630280\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=C(O)C(=O)CC[C@@]43C)[C@@H]1CCC(=O)O2\tCHEMBL1630909\n",
      "C[C@]12CC[C@H]3[C@@H](CC=C4CCCC[C@]43C)[C@@H]1CCC2=O\tCHEMBL1630910\n",
      "C[C@]12CC[C@H]3[C@@H](CC=C4CCCC[C@@]43CO)[C@@H]1CCC2=O\tCHEMBL1630911\n",
      "N#Cc1ccc(C2CCCc3cncn32)cc1\tCHEMBL9298\n",
      "CCC[C@H]1C[C@H]2OC(=O)c3c(cc(OC)c(OC)c3O)[C@H]2O1\tCHEMBL488513\n",
      "CCC[C@H]1C[C@H]2OC(=O)c3c(cc(OC)c(O)c3O)[C@H]2O1\tCHEMBL1668326\n",
      "CCC[C@H](O)C[C@@H]1Cc2cc(OC)c(OC)c(O)c2C(=O)O1\tCHEMBL1668327\n",
      "CCC[C@H](O)C[C@@H]1Cc2cc(OC)c(O)c(O)c2C(=O)O1\tCHEMBL1668328\n",
      "CCC[C@@H](O)C[C@H]1OC(=O)c2c(cc(OC)c(OC)c2O)[C@H]1O\tCHEMBL488848\n",
      "CCCC(=O)CC[C@H]1OC(=O)c2c1cc(OC)c(OC)c2O\tCHEMBL1668329\n",
      "CC(=O)N1CCN(c2ccc(OCC3COC(Cn4ccnc4)(c4ccc(Cl)cc4Cl)O3)cc2)CC1\tCHEMBL157101\n",
      "N#Cc1ccc(N(Cc2ccc(Br)cc2)n2cnnc2)cc1\tCHEMBL108425\n",
      "N#Cc1ccc(N(Cc2ccc(OS(N)(=O)=O)cc2)n2cnnc2)cc1\tCHEMBL430879\n",
      "N#Cc1ccc(N(Cc2ccc(OS(N)(=O)=O)c(Br)c2)n2cnnc2)cc1\tCHEMBL108483\n",
      "N#Cc1ccc(Cn2cncn2)cc1-c1ccc(OS(N)(=O)=O)cc1\tCHEMBL598199\n",
      "N#Cc1ccc(Cn2cncn2)cc1-c1ccc(OS(N)(=O)=O)c(Cl)c1\tCHEMBL592131\n",
      "N#Cc1ccc(N(Cc2ccccc2)n2cnnc2)cc1-c1ccc(OS(N)(=O)=O)cc1\tCHEMBL1672974\n",
      "N#Cc1ccc(N(Cc2ccc(Br)cc2)n2cnnc2)cc1-c1ccc(OS(N)(=O)=O)cc1\tCHEMBL1672975\n",
      "N#Cc1ccc(N(Cc2ccc(OS(N)(=O)=O)cc2)n2cnnc2)cc1-c1ccccc1\tCHEMBL1672976\n",
      "N#Cc1ccc(N(Cc2ccc(OS(N)(=O)=O)c(F)c2)n2cnnc2)cc1-c1ccccc1\tCHEMBL1672977\n",
      "N#Cc1ccc(N(Cc2ccc(OS(N)(=O)=O)c(Cl)c2)n2cnnc2)cc1-c1ccccc1\tCHEMBL1672978\n",
      "N#Cc1ccc(N(Cc2ccc(OS(N)(=O)=O)c(Br)c2)n2cnnc2)cc1-c1ccccc1\tCHEMBL1672979\n",
      "COc1ccc(-c2cc(N(Cc3ccc(OS(N)(=O)=O)cc3)n3cnnc3)ccc2C#N)cc1\tCHEMBL1672980\n",
      "N#Cc1ccc(N(Cc2ccc(OS(N)(=O)=O)cc2)n2cnnc2)cc1-c1ccc(F)cc1\tCHEMBL1672981\n",
      "N#Cc1ccc(-c2cc(N(Cc3ccc(OS(N)(=O)=O)cc3)n3cnnc3)ccc2C#N)cc1\tCHEMBL1672982\n",
      "O=c1cc(Cn2ccnc2)c2ccc(OCc3ccccc3)cc2o1\tCHEMBL225499\n",
      "Cc1cccc(COc2ccc3c(Cn4ccnc4)cc(=O)oc3c2)c1\tCHEMBL1688893\n",
      "O=c1cc(Cn2ccnc2)c2ccc(OCc3cccc(F)c3)cc2o1\tCHEMBL1688891\n",
      "O=c1cc(Cn2ccnc2)c2ccc(OCc3cccc(Cl)c3)cc2o1\tCHEMBL1688890\n",
      "O=c1cc(Cn2ccnc2)c2ccc(OCc3cccc(C(F)(F)F)c3)cc2o1\tCHEMBL1688892\n",
      "O=c1cc(Cn2ccnc2)c2ccc(OCc3cccc(OC(F)(F)F)c3)cc2o1\tCHEMBL1688894\n",
      "O=c1cc(Cn2ccnc2)c2ccc(OCc3cccc([N+](=O)[O-])c3)cc2o1\tCHEMBL1688895\n",
      "O=c1cc(Cn2ccnc2)c2ccc(OCc3ccc(F)cc3)cc2o1\tCHEMBL1688896\n",
      "O=c1cc(Cn2ccnc2)c2ccc(OCc3ccc(Cl)cc3)cc2o1\tCHEMBL1688897\n",
      "COc1ccc(COc2ccc3c(Cn4ccnc4)cc(=O)oc3c2)cc1\tCHEMBL1688898\n",
      "O=c1cc(Cn2ccnc2)c2ccc(OCc3ccc(OC(F)(F)F)cc3)cc2o1\tCHEMBL1688899\n",
      "O=c1cc(Cn2ccnc2)c2ccc(OCc3cc(F)cc(F)c3)cc2o1\tCHEMBL1688900\n",
      "O=c1cc(Cn2ccnc2)c2ccc(OCc3ccc(F)c(F)c3)cc2o1\tCHEMBL1688901\n",
      "O=c1cc(Cn2ccnc2)c2ccc(Oc3ccccc3)cc2o1\tCHEMBL437731\n",
      "O=c1cc(Cn2ccnc2)c2ccc(Oc3cccc(F)c3)cc2o1\tCHEMBL1688902\n",
      "O=c1cc(Cn2ccnc2)c2ccc(Oc3cccc(Cl)c3)cc2o1\tCHEMBL1688903\n",
      "COc1cccc(Oc2ccc3c(Cn4ccnc4)cc(=O)oc3c2)c1\tCHEMBL1688904\n",
      "Cc1ccc(Oc2ccc3c(Cn4ccnc4)cc(=O)oc3c2)cc1\tCHEMBL1688905\n",
      "O=c1cc(Cn2ccnc2)c2ccc(Oc3ccc(Cl)cc3)cc2o1\tCHEMBL1688906\n",
      "N#Cc1ccc(Oc2ccc3c(Cn4ccnc4)cc(=O)oc3c2)cc1\tCHEMBL1688907\n",
      "CC(=O)c1ccc(Oc2ccc3c(Cn4ccnc4)cc(=O)oc3c2)cc1\tCHEMBL1688908\n",
      "CN(C)c1ccc(Oc2ccc3c(Cn4ccnc4)cc(=O)oc3c2)cc1\tCHEMBL1688909\n",
      "O=c1cc(Cn2ccnc2)c2ccc(Oc3cc(F)cc(F)c3)cc2o1\tCHEMBL1688910\n",
      "O=c1cc(Cn2ccnc2)c2ccc(Oc3ccc(F)c(F)c3)cc2o1\tCHEMBL1688911\n",
      "O=c1cc(Cn2ccnc2)c2ccc(Nc3ccccc3)cc2o1\tCHEMBL1688912\n",
      "O=c1cc(Cn2ccnc2)c2ccc(O)cc2o1\tCHEMBL1688913\n",
      "COc1ccc2c(Cn3ccnc3)cc(=O)oc2c1\tCHEMBL225472\n",
      "COc1ccc2c(C(c3ccccc3)n3ccnc3)cc(=O)oc2c1\tCHEMBL1688914\n",
      "O=c1cc(C(c2ccccc2)n2ccnc2)c2ccc(Oc3ccccc3)cc2o1\tCHEMBL1688915\n",
      "O=c1cc(C(c2ccccc2)n2ccnc2)c2ccc(Oc3ccc(F)c(F)c3)cc2o1\tCHEMBL1688916\n",
      "O=c1cc(C(c2ccc(Cl)cc2)n2ccnc2)c2ccc(Oc3ccccc3)cc2o1\tCHEMBL1688917\n",
      "N#Cc1ccc(C(c2cc(=O)oc3cc(Oc4ccccc4)ccc23)n2ccnc2)cc1\tCHEMBL1688918\n",
      "COc1ccc2cc(Cn3ccnc3)c(=O)oc2c1\tCHEMBL387892\n",
      "O=c1oc2cc(Oc3ccccc3)ccc2cc1C(c1ccccc1)n1ccnc1\tCHEMBL1688919\n",
      "N#Cc1ccc(C2CCCc3cncn32)cc1\tCHEMBL9298\n",
      "O=C(/C=C/c1ccc(O)c(O)c1)O[C@H]1[C@H](O)[C@@H](O[C@@H]2OC[C@](O)(CO)[C@H]2O)[C@H](OCCc2ccc(O)cc2)O[C@@H]1CO\tCHEMBL1689257\n",
      "O=C(/C=C/c1ccc(O)c(O)c1)OC[C@H]1O[C@@H](OCCc2ccc(O)c(O)c2)[C@H](O[C@@H]2OC[C@](O)(CO)[C@H]2O)[C@@H](O)[C@@H]1O\tCHEMBL1689258\n",
      "O=C(/C=C/c1ccc(O)c(O)c1)O[C@H]1[C@H](O)[C@@H](CO)O[C@@H](OCCc2ccc(O)c(O)c2)[C@@H]1O[C@@H]1OC[C@](O)(CO)[C@H]1O\tCHEMBL1689259\n",
      "O=C(/C=C/c1ccc(O)c(O)c1)OC[C@H]1O[C@@H](OCCc2ccc(O)c(O)c2)[C@H](O)[C@@H](O)[C@@H]1O\tCHEMBL518414\n",
      "N#Cc1ccc(C2CCCc3cncn32)cc1\tCHEMBL9298\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL1444\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "O=C1CC(c2ccccc2)Oc2c1ccc1ccccc21\tCHEMBL404225\n",
      "N#Cc1ccc(C2CC(=O)c3ccc4ccccc4c3O2)cc1\tCHEMBL255108\n",
      "O=C1CC(c2ccc(O)cc2)Oc2c1ccc1ccccc21\tCHEMBL256665\n",
      "c1ccc(C2CC(n3ccnc3)c3ccc4ccccc4c3O2)cc1\tCHEMBL1783176\n",
      "N#Cc1ccc(C2CC(n3ccnc3)c3ccc4ccccc4c3O2)cc1\tCHEMBL1783178\n",
      "N#Cc1ccc(C2CC(n3ccnc3)c3ccc4ccccc4c3O2)cc1\tCHEMBL1783178\n",
      "N#Cc1ccc(C2CC(n3ccnc3)c3ccc4ccccc4c3O2)cc1\tCHEMBL1783178\n",
      "Oc1ccc(C2CC(n3ccnc3)c3ccc4ccccc4c3O2)cc1\tCHEMBL1783177\n",
      "COc1ccc2c(c1)OC(c1ccccc1)CC2n1ccnc1\tCHEMBL95109\n",
      "COc1ccc2c(c1)OC(c1ccccc1)CC2n1ccnc1\tCHEMBL95109\n",
      "COc1ccc2c(c1)OC(c1ccccc1)CC2n1ccnc1\tCHEMBL95109\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL1444\n",
      "O=C(C(=Cc1ccccc1)n1cncn1)c1ccc(Cl)cc1Cl\tCHEMBL1784788\n",
      "COc1ccc(C(=O)OC(COc2ccccc2)Cn2ccnc2)cc1\tCHEMBL1485809\n",
      "O=S(=O)(NCCCn1ccnc1)c1ccc2c(c1)OCCO2\tCHEMBL1784789\n",
      "O=c1oc2cc(OCC(O)Cn3cncn3)ccc2c2c1CCC2\tCHEMBL1784790\n",
      "COc1cc(CC(=O)N2CCOC(Cn3ccnc3)C2)cc(OC)c1OC\tCHEMBL1784791\n",
      "COc1ccc2c(c1)OC1(CCN(C(=O)Cn3cncn3)CC1)CC2=O\tCHEMBL1784792\n",
      "O=C(Cn1cncn1)NC(CCc1ccccc1)c1ccc2c(c1)OCCO2\tCHEMBL1784793\n",
      "CCc1[nH]c2ccc(C(=O)NCC(C)n3cncn3)cc2c1C\tCHEMBL1784794\n",
      "CC(CNC(=O)c1cc2c([nH]c1=O)CCCCC2)n1cncn1\tCHEMBL1784795\n",
      "Cc1ncc(CNc2ccc(OCC(O)Cn3ccnc3)cc2)cn1.O=C(O)CC(O)(CC(=O)O)C(=O)O\tCHEMBL1784796\n",
      "O=C(CCn1ccnc1)NC[C@H]1CC[C@H](c2ncncc2-c2ccccn2)CC1\tCHEMBL1784797\n",
      "O=C(Cn1ccnc1)Nc1ccccc1Oc1ccc(F)cc1\tCHEMBL1784798\n",
      "COc1ccc(C(C)NC(=O)c2ccccc2Cn2cncn2)cc1F\tCHEMBL1784800\n",
      "COc1ccc(S(=O)(=O)N2CCCC(Cn3ccnc3)C2)cc1C(C)(C)C\tCHEMBL1784801\n",
      "COc1cc2c(cc1-c1cncn1CCCn1ccnc1C)OCCCO2.Cl\tCHEMBL1784803\n",
      "COc1ccc(S(=O)(=O)N2CCOC(Cn3ccnc3)C2)cc1.Cc1ccc(S(=O)(=O)O)cc1\tCHEMBL1784804\n",
      "CC(=O)c1ccc(S(=O)(=O)N2CCOC(Cn3ccnc3)C2)cc1\tCHEMBL1784805\n",
      "Cc1ccc(C(=O)NCCCn2ccnc2)cc1[N+](=O)[O-]\tCHEMBL1521315\n",
      "Cc1ccc(C(=O)NCCCn2ccnc2)cn1\tCHEMBL1389987\n",
      "CCOc1ccc(S(=O)(=O)NCCCn2ccnc2)c2ccccc12\tCHEMBL1784806\n",
      "Cc1cc(C)c(S(=O)(=O)NCCCn2ccnc2)cc1C\tCHEMBL1303900\n",
      "COc1ccc(S(=O)(=O)NCCCn2ccnc2)cc1C\tCHEMBL1784807\n",
      "COc1cc(Cl)c(S(=O)(=O)NCCCn2ccnc2)cc1Cl\tCHEMBL1351273\n",
      "COc1ccc(S(=O)(=O)NCCCn2ccnc2)cc1C(C)(C)C\tCHEMBL1784808\n",
      "C=CCN(c1ccc(C(=O)NCCCn2ccnc2)cc1)S(C)(=O)=O\tCHEMBL1538978\n",
      "COc1ccc(S(=O)(=O)NCCCn2ccnc2)cc1F\tCHEMBL1784809\n",
      "CC(C)(C(=O)Nc1ccccc1Oc1ccc(F)cc1)n1cncn1\tCHEMBL1784810\n",
      "O=C(Cc1ccc(F)cc1)N1CCOC(Cn2ccnc2)C1\tCHEMBL1784811\n",
      "O=C(Cc1ccc2c(c1)OCO2)N1CCOC(Cn2ccnc2)C1\tCHEMBL1784812\n",
      "CC1(C)Oc2ccc(S(=O)(=O)NCCCn3ccnc3)cc2NC1=O\tCHEMBL1784813\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2ccnn2)cc1\tCHEMBL224789\n",
      "CCCc1cn(C(c2ccc(C#N)cc2)c2ccc(C#N)cc2)nn1\tCHEMBL1824742\n",
      "CCCCCCc1cn(C(c2ccc(C#N)cc2)c2ccc(C#N)cc2)nn1\tCHEMBL1824743\n",
      "CCCCCCCc1cn(C(c2ccc(C#N)cc2)c2ccc(C#N)cc2)nn1\tCHEMBL1824744\n",
      "CCCCCCCCCCc1cn(C(c2ccc(C#N)cc2)c2ccc(C#N)cc2)nn1\tCHEMBL1824745\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cc(C3CCCCC3)nn2)cc1\tCHEMBL1824746\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cc(-c3ccccc3)nn2)cc1\tCHEMBL1824747\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cc(COc3ccccc3)nn2)cc1\tCHEMBL1824748\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cc(COc3ccc(Cl)cc3)nn2)cc1\tCHEMBL1824749\n",
      "N#Cc1ccc(OCc2cn(C(c3ccc(C#N)cc3)c3ccc(C#N)cc3)nn2)cc1\tCHEMBL1824750\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cc(COc3ccc([N+](=O)[O-])cc3)nn2)cc1\tCHEMBL1824751\n",
      "Cc1ccc(OCc2cn(C(c3ccc(C#N)cc3)c3ccc(C#N)cc3)nn2)cc1\tCHEMBL1824752\n",
      "COc1ccc(OCc2cn(C(c3ccc(C#N)cc3)c3ccc(C#N)cc3)nn2)cc1\tCHEMBL1824753\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cc(COc3ccc(-c4ccccc4)cc3)nn2)cc1\tCHEMBL1824754\n",
      "c1ccc(C(c2ccccc2)n2ccnn2)cc1\tCHEMBL1824755\n",
      "CCCc1cn(C(c2ccccc2)c2ccccc2)nn1\tCHEMBL1824756\n",
      "CCCCCCc1cn(C(c2ccccc2)c2ccccc2)nn1\tCHEMBL1824757\n",
      "CCCCCCCc1cn(C(c2ccccc2)c2ccccc2)nn1\tCHEMBL1824758\n",
      "CCCCCCCCCCc1cn(C(c2ccccc2)c2ccccc2)nn1\tCHEMBL1824759\n",
      "c1ccc(C(c2ccccc2)n2cc(C3CCCCC3)nn2)cc1\tCHEMBL1824760\n",
      "c1ccc(-c2cn(C(c3ccccc3)c3ccccc3)nn2)cc1\tCHEMBL1824761\n",
      "c1ccc(OCc2cn(C(c3ccccc3)c3ccccc3)nn2)cc1\tCHEMBL1824762\n",
      "Clc1ccc(OCc2cn(C(c3ccccc3)c3ccccc3)nn2)cc1\tCHEMBL1824763\n",
      "N#Cc1ccc(OCc2cn(C(c3ccccc3)c3ccccc3)nn2)cc1\tCHEMBL1824764\n",
      "O=[N+]([O-])c1ccc(OCc2cn(C(c3ccccc3)c3ccccc3)nn2)cc1\tCHEMBL1824765\n",
      "Cc1ccc(OCc2cn(C(c3ccccc3)c3ccccc3)nn2)cc1\tCHEMBL1824766\n",
      "COc1ccc(OCc2cn(C(c3ccccc3)c3ccccc3)nn2)cc1\tCHEMBL1824767\n",
      "c1ccc(-c2ccc(OCc3cn(C(c4ccccc4)c4ccccc4)nn3)cc2)cc1\tCHEMBL1824768\n",
      "N#Cc1ccc(Cn2ccnn2)cc1\tCHEMBL1824769\n",
      "N#Cc1ccc(Cn2nccn2)cc1\tCHEMBL1825017\n",
      "c1ccc(Cn2ccnn2)cc1\tCHEMBL1825018\n",
      "c1ccc(Cn2nccn2)cc1\tCHEMBL1825019\n",
      "N#Cc1ccc(Cn2cncn2)cc1\tCHEMBL1825020\n",
      "N#Cc1ccc(Cn2ccnc2)cc1\tCHEMBL349822\n",
      "c1ccc(Cn2cncn2)cc1\tCHEMBL1825021\n",
      "c1ccc(Cn2ccnc2)cc1\tCHEMBL14192\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2nccn2)cc1\tCHEMBL1825022\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL1444\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2ccnc2)cc1\tCHEMBL29482\n",
      "c1ccc(C(c2ccccc2)n2nccn2)cc1\tCHEMBL1825023\n",
      "c1ccc(C(c2ccccc2)n2cncn2)cc1\tCHEMBL1825024\n",
      "c1ccc(C(c2ccccc2)n2ccnc2)cc1\tCHEMBL336638\n",
      "CCCc1cn(Cc2ccc(C#N)cc2)nn1\tCHEMBL1825025\n",
      "CCCCCCc1cn(Cc2ccc(C#N)cc2)nn1\tCHEMBL1825026\n",
      "CCCCCCCc1cn(Cc2ccc(C#N)cc2)nn1\tCHEMBL1825027\n",
      "CCCCCCCCCCc1cn(Cc2ccc(C#N)cc2)nn1\tCHEMBL1825028\n",
      "N#Cc1ccc(Cn2cc(C3CCCCC3)nn2)cc1\tCHEMBL1825029\n",
      "N#Cc1ccc(Cn2cc(-c3ccccc3)nn2)cc1\tCHEMBL1825030\n",
      "N#Cc1ccc(Cn2cc(COc3ccccc3)nn2)cc1\tCHEMBL1825031\n",
      "N#Cc1ccc(Cn2cc(COc3ccc(Cl)cc3)nn2)cc1\tCHEMBL1825032\n",
      "N#Cc1ccc(Cn2cc(COc3ccc(C#N)cc3)nn2)cc1\tCHEMBL1825033\n",
      "N#Cc1ccc(Cn2cc(COc3ccc([N+](=O)[O-])cc3)nn2)cc1\tCHEMBL1825034\n",
      "Cc1ccc(OCc2cn(Cc3ccc(C#N)cc3)nn2)cc1\tCHEMBL1825035\n",
      "COc1ccc(OCc2cn(Cc3ccc(C#N)cc3)nn2)cc1\tCHEMBL1825036\n",
      "N#Cc1ccc(Cn2cc(COc3ccc(-c4ccccc4)cc3)nn2)cc1\tCHEMBL1825037\n",
      "CCCc1cn(Cc2ccccc2)nn1\tCHEMBL1825038\n",
      "CCCCCCc1cn(Cc2ccccc2)nn1\tCHEMBL1825039\n",
      "CCCCCCCc1cn(Cc2ccccc2)nn1\tCHEMBL1825040\n",
      "CCCCCCCCCCc1cn(Cc2ccccc2)nn1\tCHEMBL1825041\n",
      "c1ccc(Cn2cc(C3CCCCC3)nn2)cc1\tCHEMBL1825042\n",
      "c1ccc(Cn2cc(-c3ccccc3)nn2)cc1\tCHEMBL1825043\n",
      "c1ccc(Cn2cc(COc3ccccc3)nn2)cc1\tCHEMBL272339\n",
      "Clc1ccc(OCc2cn(Cc3ccccc3)nn2)cc1\tCHEMBL1825044\n",
      "N#Cc1ccc(OCc2cn(Cc3ccccc3)nn2)cc1\tCHEMBL1825045\n",
      "O=[N+]([O-])c1ccc(OCc2cn(Cc3ccccc3)nn2)cc1\tCHEMBL1825046\n",
      "Cc1ccc(OCc2cn(Cc3ccccc3)nn2)cc1\tCHEMBL1825047\n",
      "COc1ccc(OCc2cn(Cc3ccccc3)nn2)cc1\tCHEMBL1825048\n",
      "c1ccc(Cn2cc(COc3ccc(-c4ccccc4)cc3)nn2)cc1\tCHEMBL1825049\n",
      "O=C1/C(=C\\c2cccnc2)C(c2ccc(Cl)cc2)Cn2cccc21\tCHEMBL1098737\n",
      "O=C1/C(=C\\c2ccncc2)C(c2ccc(Cl)cc2)Cn2cccc21\tCHEMBL9165\n",
      "C=CCOC(=O)N1Cc2[nH]c3ccccc3c2C[C@H]1C(=O)OC\tCHEMBL1915151\n",
      "COC(=O)[C@@H]1Cc2c([nH]c3ccccc23)CN1CC(C)C\tCHEMBL1915152\n",
      "COC(=O)[C@@H]1Cc2c([nH]c3ccccc23)CN1S(=O)(=O)c1ccc(C)cc1\tCHEMBL1915156\n",
      "COC(=O)[C@H]1Cc2c([nH]c3ccccc23)CN1S(C)(=O)=O\tCHEMBL1915159\n",
      "COC(=O)[C@@H]1Cc2c([nH]c3ccccc23)CN1CC1CC1\tCHEMBL1915161\n",
      "COC(=O)[C@@H]1Cc2c([nH]c3ccccc23)CN1C(=O)NC(C)C\tCHEMBL1915167\n",
      "CC(C)CN1Cc2[nH]c3ccccc3c2C[C@H]1C(=O)O\tCHEMBL1915176\n",
      "COC(=O)[C@@H]1Cc2c([nH]c3ccccc23)CN1Cc1ccc(O)cc1\tCHEMBL1915401\n",
      "O=C(O)[C@@H]1Cc2c([nH]c3ccccc23)CN1Cc1ccc(O)cc1\tCHEMBL1915403\n",
      "O=C1C[C@@H](c2ccc(O)cc2)Oc2cc(O)cc(O)c21\tCHEMBL9352\n",
      "c1ccc(-c2nsc(-c3ccccc3)n2)cc1\tCHEMBL264401\n",
      "c1cncc(-c2nsc(-c3cccnc3)n2)c1\tCHEMBL1939366\n",
      "c1cc(-c2nsc(-c3ccncc3)n2)ccn1\tCHEMBL1454842\n",
      "Clc1ccc(-c2nsc(-c3ccc(Cl)cc3)n2)cc1\tCHEMBL1939519\n",
      "Fc1ccc(-c2nsc(-c3ccc(F)cc3)n2)cc1\tCHEMBL1939520\n",
      "Brc1ccc(-c2nsc(-c3ccc(Br)cc3)n2)cc1\tCHEMBL1939521\n",
      "COc1ccc(-c2nsc(-c3ccc(OC)cc3)n2)cc1\tCHEMBL1939522\n",
      "Brc1ccccc1-c1nsc(-c2ccccc2Br)n1\tCHEMBL1939523\n",
      "Clc1ccccc1-c1nsc(-c2ccccc2Cl)n1\tCHEMBL1939524\n",
      "FC(F)(F)c1ccc(-c2nsc(-c3ccc(C(F)(F)F)cc3)n2)cc1\tCHEMBL1939525\n",
      "CC(C)(C)c1ccc(-c2nsc(-c3ccc(C(C)(C)C)cc3)n2)cc1\tCHEMBL1939526\n",
      "Ic1ccc(-c2nsc(-c3ccc(I)cc3)n2)cc1\tCHEMBL1939527\n",
      "Ic1cccc(-c2nsc(-c3cccc(I)c3)n2)c1\tCHEMBL1939528\n",
      "Fc1cccc(-c2nsc(-c3cccc(F)c3)n2)c1\tCHEMBL1939529\n",
      "Fc1ccccc1-c1nsc(-c2ccccc2F)n1\tCHEMBL1939530\n",
      "Brc1cccc(-c2nsc(-c3cccc(Br)c3)n2)c1\tCHEMBL1939531\n",
      "Clc1cccc(-c2nsc(-c3cccc(Cl)c3Cl)n2)c1Cl\tCHEMBL1939532\n",
      "Cc1cc(Cl)ccc1-c1nsc(-c2ccc(Cl)cc2C)n1\tCHEMBL1939533\n",
      "Cc1cccc(-c2nsc(-c3cccc(C)c3)n2)c1\tCHEMBL1939534\n",
      "FC(F)(F)c1ccccc1-c1nsc(-c2ccccc2C(F)(F)F)n1\tCHEMBL1939535\n",
      "Clc1ccc(Cl)c(-c2nsc(-c3cc(Cl)ccc3Cl)n2)c1\tCHEMBL1939536\n",
      "Clc1ccc(-c2nsc(-c3ccc(Cl)c(Cl)c3)n2)cc1Cl\tCHEMBL1939537\n",
      "Cc1c(Cl)cccc1-c1nsc(-c2cccc(Cl)c2C)n1\tCHEMBL1939538\n",
      "Cc1ccc(-c2nsc(-c3ccc(C)cc3)n2)cc1\tCHEMBL1939539\n",
      "c1ccc2cc(-c3nsc(-c4ccc5ccccc5c4)n3)ccc2c1\tCHEMBL1939540\n",
      "CCCCc1ccc(-c2nsc(-c3ccc(CCCC)cc3)n2)cc1\tCHEMBL1939541\n",
      "Cc1ccc(Cl)cc1-c1nsc(-c2cc(Cl)ccc2C)n1\tCHEMBL1939542\n",
      "Cc1cc(Br)ccc1-c1nsc(-c2ccc(Br)cc2C)n1\tCHEMBL1939543\n",
      "Ic1ccccc1-c1nsc(-c2ccccc2I)n1\tCHEMBL1939544\n",
      "Cc1ccc(-c2nsc(-c3ccc(C)cc3Br)n2)c(Br)c1\tCHEMBL1939545\n",
      "Clc1ccc(-c2nsc(-c3ccc(Cl)cc3Cl)n2)c(Cl)c1\tCHEMBL1939546\n",
      "O=[N+]([O-])c1ccc(-c2nsc(-c3ccc([N+](=O)[O-])cc3)n2)cc1\tCHEMBL1939547\n",
      "N#Cc1ccc(-c2nsc(-c3ccc(C#N)cc3)n2)cc1\tCHEMBL1939548\n",
      "N#Cc1cccc(-c2nsc(-c3cccc(C#N)c3)n2)c1\tCHEMBL1939549\n",
      "COc1cccc(-c2nsc(-c3cccc(OC)c3)n2)c1\tCHEMBL1939550\n",
      "Oc1ccc(-c2nsc(-c3ccc(O)cc3)n2)cc1\tCHEMBL1939551\n",
      "Oc1cccc(-c2nsc(-c3cccc(O)c3)n2)c1\tCHEMBL503399\n",
      "Nc1ccc(-c2nsc(-c3ccc(N)cc3)n2)cc1\tCHEMBL1939552\n",
      "Oc1ccc(/C=C/c2cc(O)cc(O)c2)cc1\tCHEMBL165\n",
      "Oc1ccc(/C=C\\c2cc(O)cc(O)c2)cc1\tCHEMBL87333\n",
      "Oc1ccc(CCc2cc(O)cc(O)c2)cc1\tCHEMBL111234\n",
      "O=C1C[C@@H](c2ccc(O)cc2)Oc2cc(O)cc(O)c21\tCHEMBL9352\n",
      "N#Cc1ccc(Oc2cccnc2)cc1\tCHEMBL1956171\n",
      "N#Cc1ccc(Oc2cccnc2)cc1C#N\tCHEMBL1728910\n",
      "N#Cc1ccc(Oc2cccnc2)cc1NCc1ccccc1\tCHEMBL1956175\n",
      "N#Cc1ccc(Oc2cccnc2)cc1Oc1cccnc1\tCHEMBL1956179\n",
      "N#Cc1ccc(Oc2cccnc2)c(Oc2cccc(Cl)c2)c1\tCHEMBL1956180\n",
      "N#Cc1ccc(-n2ccc3ccncc32)cc1\tCHEMBL1955869\n",
      "Clc1ccc(-n2ccc3ccncc32)cc1\tCHEMBL1956183\n",
      "Cc1cc(-n2ccc3ccncc32)ccc1Cl\tCHEMBL1956186\n",
      "N#Cc1ccc(-n2ccc3ccncc32)cc1Oc1ccccc1\tCHEMBL1956188\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL1444\n",
      "Brc1cccc(-c2nc(-c3cccnc3)ns2)c1\tCHEMBL1957208\n",
      "COc1cccc(-c2nc(-c3cccnc3)ns2)c1\tCHEMBL1957209\n",
      "c1ccc(-c2nc(-c3cccnc3)cs2)cc1\tCHEMBL1601919\n",
      "Fc1ccc(-c2nc(-c3cccnc3)cs2)cc1\tCHEMBL1957210\n",
      "Clc1ccc(-c2nc(-c3cccnc3)cs2)cc1\tCHEMBL1957211\n",
      "Brc1ccc(-c2nc(-c3cccnc3)cs2)cc1\tCHEMBL1957212\n",
      "COc1ccc(-c2nc(-c3cccnc3)cs2)cc1\tCHEMBL1957213\n",
      "c1cncc(-c2csc(-c3ccncc3)n2)c1\tCHEMBL1957214\n",
      "Oc1ccc(-c2nc(-c3cccnc3)cs2)cc1\tCHEMBL1957215\n",
      "Clc1cc(-c2nc(-c3cccnc3)cs2)ccn1\tCHEMBL1957216\n",
      "COc1cc(-c2nc(-c3cccnc3)cs2)ccn1\tCHEMBL1957217\n",
      "Cc1cnccc1-c1nc(-c2cccnc2)cs1\tCHEMBL1957218\n",
      "Brc1ccc(-c2csc(-c3ccncc3)n2)cn1\tCHEMBL1957219\n",
      "Brc1ccc(-c2csc(-c3cccnc3)n2)cn1\tCHEMBL1957220\n",
      "COc1ccc(-c2csc(-c3ccncc3)n2)cn1\tCHEMBL1957221\n",
      "CCOc1ccc(-c2csc(-c3ccncc3)n2)cn1\tCHEMBL1957222\n",
      "COc1ccc(-c2csc(-c3cccnc3)n2)cn1\tCHEMBL1957223\n",
      "Oc1ccc(/C=C/c2cc(O)cc(O)c2)cc1\tCHEMBL165\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL1444\n",
      "CC(C)(C#N)c1cc(Cn2cncn2)cc(C(C)(C)C#N)c1\tCHEMBL1399\n",
      "N#Cc1ccc(C2CCCc3cncn32)cc1\tCHEMBL9298\n",
      "CC(C)(C#N)c1cc(Cn2cncn2)cc(C(C)(C)C#N)c1\tCHEMBL1399\n",
      "O=c1cc(-c2ccccc2)oc2cc(O)cc(O)c12\tCHEMBL117\n",
      "COc1ccc(-c2coc3cc(O)cc(O)c3c2=O)cc1\tCHEMBL131921\n",
      "O=C1c2ccccc2OC(O)C1c1ccccc1\tCHEMBL2011191\n",
      "O=C1c2ccccc2OCC1c1ccc(O)cc1\tCHEMBL2011192\n",
      "O=C1c2ccccc2OCC1c1ccc(O)c(O)c1\tCHEMBL2011193\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=CC(=O)CC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL274826\n",
      "O=C1c2ccccc2OCC1c1ccccc1\tCHEMBL2011171\n",
      "Cc1ccc2c(c1)C(=O)C(c1ccccc1)CO2\tCHEMBL2011172\n",
      "Cc1ccc2c(c1)OCC(c1ccccc1)C2=O\tCHEMBL2011173\n",
      "Cc1cccc(C2COc3ccccc3C2=O)c1\tCHEMBL2011174\n",
      "Cc1ccc(C2COc3ccccc3C2=O)cc1\tCHEMBL2011175\n",
      "O=C1c2c(ccc3ccccc23)OCC1c1ccccc1\tCHEMBL2011176\n",
      "O=C1c2ccccc2OCC1c1ccc(Oc2ccccc2)cc1\tCHEMBL2011177\n",
      "COc1ccc2c(c1)C(=O)C(c1ccccc1)CO2\tCHEMBL2011180\n",
      "COc1ccc2c(c1)OCC(c1ccccc1)C2=O\tCHEMBL1165711\n",
      "COc1ccccc1C1COc2ccccc2C1=O\tCHEMBL2011181\n",
      "COc1cccc(C2COc3ccccc3C2=O)c1\tCHEMBL2011182\n",
      "COc1ccc(C2COc3ccccc3C2=O)cc1\tCHEMBL2011183\n",
      "COc1cc(OC)cc(C2COc3ccccc3C2=O)c1\tCHEMBL2011184\n",
      "O=C1c2ccccc2OCC1c1ccsc1\tCHEMBL2011185\n",
      "O=C1c2ccccc2OCC1c1cccnc1\tCHEMBL2011186\n",
      "O=C1c2cc(F)ccc2OCC1c1ccccc1\tCHEMBL2011187\n",
      "O=C1c2cc(Cl)ccc2OCC1c1ccccc1\tCHEMBL2011188\n",
      "O=C1c2cc(Br)ccc2OCC1c1ccccc1\tCHEMBL2011189\n",
      "CC(C)(C)c1ccc2c(c1)C(=O)C(c1ccccc1)CO2\tCHEMBL2011190\n",
      "CC(=O)N1CCN(c2ccc(OCC3COC(Cn4ccnc4)(c4ccc(Cl)cc4Cl)O3)cc2)CC1\tCHEMBL157101\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=C[C@@H](O)CC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL1077603\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=CCCC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL1629804\n",
      "C[C@]12CC[C@H]3[C@@H](CC[C@@]45O[C@@H]4CCC[C@]35C)[C@@H]1CCC2=O\tCHEMBL2058266\n",
      "C[C@]12CC[C@H]3[C@@H](CC[C@H]4C=CCC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL1629800\n",
      "C[C@]12CC[C@H]3[C@@H](CC[C@H]4[C@@H]5O[C@@H]5CC[C@]34C)[C@@H]1CCC2=O\tCHEMBL1629802\n",
      "C[C@]12CC[C@H]3[C@@H](CC[C@H]4CC=CC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL2058267\n",
      "C[C@]12CC[C@H]3[C@@H](CC[C@H]4C[C@@H]5O[C@@H]5C[C@@]43C)[C@@H]1CCC2=O\tCHEMBL2058268\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=C(O)C(=O)CC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL132530\n",
      "CC(C)C(c1cc2c3c(c1)CCN3C(=O)CC2)n1ccnc1\tCHEMBL2163638\n",
      "O=C1CCc2cc(C(C3CC3)n3ccnc3)cc3c2N1CC3\tCHEMBL2163639\n",
      "COc1ccc(C(c2cc3c4c(c2)CCN4C(=O)CC3)n2ccnc2)cc1\tCHEMBL2163646\n",
      "N#Cc1ccc(C2CCCc3cncn32)cc1\tCHEMBL9298\n",
      "C=C(c1cc2c3c(c1)CCN3C(=O)CC2)n1ccnc1\tCHEMBL2163641\n",
      "N#Cc1ccc(C2CCCc3cncn32)cc1\tCHEMBL9298\n",
      "O=C1CCc2cc(-c3cccnc3)cc(-c3ccsc3)c2N1\tCHEMBL2165325\n",
      "O=C1CCc2cc(-c3cccnc3)cc(-c3cccnc3)c2N1\tCHEMBL2165324\n",
      "O=C1CCc2cc(-c3cccnc3)cc(-c3ccccc3)c2N1\tCHEMBL2165323\n",
      "O=C1CCc2cc(-c3cccnc3)cc(Br)c2N1\tCHEMBL2165322\n",
      "CN1C(=O)CCc2cc(-c3cccnc3)c(OCc3ccccc3)cc21\tCHEMBL2165321\n",
      "O=C1CCc2cc(-c3cccnc3)c(OCc3ccccc3)cc2N1\tCHEMBL2165320\n",
      "CN1C(=O)CCc2cc(-c3cccnc3)c(OC3CCCC3)cc21\tCHEMBL2165319\n",
      "O=C1CCc2cc(-c3cccnc3)c(OC3CCCC3)cc2N1\tCHEMBL2165318\n",
      "CCOc1cc2c(cc1-c1cccnc1)CCC(=O)N2C\tCHEMBL2165317\n",
      "CCOc1cc2c(cc1-c1cccnc1)CCC(=O)N2\tCHEMBL2165316\n",
      "COc1cc2c(cc1-c1cccnc1)CCC(=O)N2C\tCHEMBL2165315\n",
      "COc1cc2c(cc1-c1cccnc1)CCC(=O)N2\tCHEMBL2165314\n",
      "CN1C(=O)CCc2cc(-c3cccnc3)ccc21\tCHEMBL446083\n",
      "O=C1CCc2cc(-c3cccnc3)c(O)cc2N1\tCHEMBL2165313\n",
      "O=C1CCc2cc(-c3cccnc3)ccc2N1\tCHEMBL62811\n",
      "O=[N+]([O-])c1ccc(Cn2ccnc2)c(Oc2ccccc2)c1\tCHEMBL1083653\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL1444\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=C(O)C(=O)CC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL132530\n",
      "C=C1C[C@@H]2[C@H](CC[C@]3(C)C(=O)CC[C@@H]23)[C@@]2(C)C=CC(=O)C=C12\tCHEMBL1200374\n",
      "C[C@]12C=CC(=O)C=C1[C@H](OCC#CCO)C[C@@H]1[C@@H]2CC[C@]2(C)C(=O)CC[C@@H]12\tCHEMBL2179114\n",
      "CCCCCCCC#CCO[C@@H]1C[C@@H]2[C@H](CC[C@]3(C)C(=O)CC[C@@H]23)[C@@]2(C)C=CC(=O)C=C12\tCHEMBL2179113\n",
      "CCCCC#CCO[C@@H]1C[C@@H]2[C@H](CC[C@]3(C)C(=O)CC[C@@H]23)[C@@]2(C)C=CC(=O)C=C12\tCHEMBL2179112\n",
      "CCCC#CCO[C@@H]1C[C@@H]2[C@H](CC[C@]3(C)C(=O)CC[C@@H]23)[C@@]2(C)C=CC(=O)C=C12\tCHEMBL2179111\n",
      "CCC#CCO[C@@H]1C[C@@H]2[C@H](CC[C@]3(C)C(=O)CC[C@@H]23)[C@@]2(C)C=CC(=O)C=C12\tCHEMBL2179110\n",
      "CC#CCO[C@@H]1C[C@@H]2[C@H](CC[C@]3(C)C(=O)CC[C@@H]23)[C@@]2(C)C=CC(=O)C=C12\tCHEMBL2179109\n",
      "CCO[C@@H]1C[C@@H]2[C@H](CC[C@]3(C)C(=O)CC[C@@H]23)[C@@]2(C)C=CC(=O)C=C12\tCHEMBL2179108\n",
      "CO[C@@H]1C[C@@H]2[C@H](CC[C@]3(C)C(=O)CC[C@@H]23)[C@@]2(C)C=CC(=O)C=C12\tCHEMBL2179107\n",
      "O=C1C[C@@H](c2ccc(O)cc2)Oc2cc(O)cc(O)c21\tCHEMBL9352\n",
      "COc1cccc(-c2csc(-c3cccc(OC)c3)n2)c1\tCHEMBL2208141\n",
      "COc1cccc(-c2csc(-c3ccccc3)n2)c1\tCHEMBL2208140\n",
      "COc1cccc(-c2nc(-c3ccccc3)cs2)c1\tCHEMBL2208131\n",
      "Fc1ccccc1-c1nsc(-c2ccccc2F)n1\tCHEMBL1939530\n",
      "c1ccc(-c2nsc(-c3ccccc3)n2)cc1\tCHEMBL264401\n",
      "Oc1ccc(/C=C/c2cc(O)cc(O)c2)cc1\tCHEMBL165\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "O=C1/C(=C/c2cccc(OCCCn3ccnc3)c2)Cc2ccccc21\tCHEMBL2235953\n",
      "O=C1/C(=C/c2ccccc2OCCCn2ccnc2)Cc2ccccc21\tCHEMBL2235952\n",
      "COc1ccc(/C=C2\\Cc3ccccc3C2=O)cc1OCCCn1ccnc1\tCHEMBL2235951\n",
      "COc1cc(/C=C2\\Cc3ccccc3C2=O)ccc1OCCCn1ccnc1\tCHEMBL2235950\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=CC(=O)CC[C@@]43C)[C@@H]1C/C(=C\\c1ccc(OCCCn3ccnc3)cc1)C2=O\tCHEMBL2282047\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=CC(=O)CC[C@@]43C)[C@@H]1C/C(=C\\c1cccc(OCCCn3ccnc3)c1)C2=O\tCHEMBL2282050\n",
      "C[C@]12CC[C@H]3[C@@H](CC=C4C[C@@H](O)CC[C@@]43C)[C@@H]1C/C(=C\\c1cccc(OCCCn3ccnc3)c1)[C@@H]2O\tCHEMBL2282053\n",
      "O=C1CCc2cc(C(c3ccncc3)c3cc(C(F)(F)F)cc(C(F)(F)F)c3)cc3c2N1CC3\tCHEMBL2312963\n",
      "Cc1cccc(C(c2ccncc2)c2cc3c4c(c2)CCN4C(=O)CC3)c1\tCHEMBL2312962\n",
      "O=C1CCc2cc(C(c3ccncc3)c3ccc(Cl)cc3)cc3c2N1CC3\tCHEMBL2312961\n",
      "O=C1CCc2cc(C(c3ccncc3)c3cccc(Cl)c3)cc3c2N1CC3\tCHEMBL2312960\n",
      "O=C1CCc2cc(C(c3ccncc3)c3ccc(F)cc3)cc3c2N1CC3\tCHEMBL2312959\n",
      "O=C1CCc2cc(C(c3ccncc3)c3cccc(F)c3)cc3c2N1CC3\tCHEMBL2312958\n",
      "COc1ccc(C(c2ccncc2)c2cc3c4c(c2)CCN4C(=O)CC3)cc1\tCHEMBL2312957\n",
      "COc1ccc(C(c2ccncc2)c2cc3c4c(c2)CCN4C(=O)CC3)cc1\tCHEMBL2312957\n",
      "COc1ccc(C(c2ccncc2)c2cc3c4c(c2)CCN4C(=O)CC3)cc1\tCHEMBL2312957\n",
      "COc1cccc(C(c2ccncc2)c2cc3c4c(c2)CCN4C(=O)CC3)c1\tCHEMBL2312956\n",
      "COc1ccccc1C(c1ccncc1)c1cc2c3c(c1)CCN3C(=O)CC2\tCHEMBL2312955\n",
      "O=C1CCc2cc(C(c3ccccc3)c3ccncc3)cc3c2N1CC3\tCHEMBL2312954\n",
      "O=C1CCc2cc(C(O)(c3ccccc3)c3ccncc3)cc3c2N1CC3\tCHEMBL2312953\n",
      "O=C1CCc2cc(C(c3ccncc3)C3CCCCC3)cc3c2N1CC3\tCHEMBL2312952\n",
      "CC(C)C(c1ccncc1)c1cc2c3c(c1)CCN3C(=O)CC2\tCHEMBL2312951\n",
      "CC(c1ccncc1)c1cc2c3c(c1)CCN3C(=O)CC2\tCHEMBL2312950\n",
      "C=C(c1ccncc1)c1cc2c3c(c1)CCN3C(=O)CC2\tCHEMBL2312949\n",
      "O=C1CCc2cc(Cc3ccncc3)cc3c2N1CC3\tCHEMBL2311553\n",
      "O=C1CCc2cc(C(O)c3ccncc3)cc3c2N1CC3\tCHEMBL2312948\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL1444\n",
      "N#Cc1ccc(C2CCCc3cncn32)cc1\tCHEMBL9298\n",
      "O=C1CCc2cc(C(C3CC3)n3ccnc3)cc3c2N1CC3\tCHEMBL2163639\n",
      "O=C1CCc2cc(C(c3ccc(F)cc3)c3cccnc3)cc3c2N1CC3\tCHEMBL2312972\n",
      "O=C1CCc2cc(C(c3cccnc3)c3cccc(Cl)c3)cc3c2N1CC3\tCHEMBL2312971\n",
      "Cc1cccc(C(c2cccnc2)c2cc3c4c(c2)CCN4C(=O)CC3)c1\tCHEMBL2312970\n",
      "COc1ccccc1C(c1cccnc1)c1cc2c3c(c1)CCN3C(=O)CC2\tCHEMBL2312969\n",
      "O=C1CCc2cc(C(c3ccccc3)c3cccnc3)cc3c2N1CC3\tCHEMBL2312968\n",
      "CC(c1cccnc1)c1cc2c3c(c1)CCN3C(=O)CC2\tCHEMBL2312967\n",
      "C=C(c1cccnc1)c1cc2c3c(c1)CCN3C(=O)CC2\tCHEMBL2312966\n",
      "O=C1CCc2cc(Cc3cccnc3)cc3c2N1CC3\tCHEMBL2312965\n",
      "O=C1CCc2cc(C(O)c3cccnc3)cc3c2N1CC3\tCHEMBL2312964\n",
      "C[C@]12CC[C@H]3[C@@H](CC=C4C[C@@H](O)CC[C@@]43C)[C@@H]1CC=C2c1cccnc1\tCHEMBL254328\n",
      "N#Cc1ccc(C2CCCc3cncn32)cc1\tCHEMBL9298\n",
      "Nc1ccc(Cn2ccnc2)c2c(=O)c3ccccc3oc12\tCHEMBL2333337\n",
      "O=c1c2ccccc2oc2c(O)ccc(Cn3ccnc3)c12\tCHEMBL2333336\n",
      "O=c1c2ccccc2oc2c(OC3CCCC3)ccc(Cn3ccnc3)c12\tCHEMBL2333335\n",
      "O=c1c2ccccc2oc2c(OCC3CC3)ccc(Cn3ccnc3)c12\tCHEMBL2333334\n",
      "CCC(C)Oc1ccc(Cn2ccnc2)c2c(=O)c3ccccc3oc12\tCHEMBL2333333\n",
      "CC(C)CCOc1ccc(Cn2ccnc2)c2c(=O)c3ccccc3oc12\tCHEMBL2333332\n",
      "CC(C)Oc1ccc(Cn2ccnc2)c2c(=O)c3ccccc3oc12\tCHEMBL2333331\n",
      "CCOc1ccc(Cn2ccnc2)c2c(=O)c3ccccc3oc12\tCHEMBL2333330\n",
      "COc1ccc(Cn2ccnc2)c2c(=O)c3ccccc3oc12\tCHEMBL2333329\n",
      "O=c1c2ccccc2oc2c(-c3ccccc3)ccc(Cn3ccnc3)c12\tCHEMBL2333328\n",
      "O=c1c2ccccc2oc2c(I)ccc(Cn3ccnc3)c12\tCHEMBL2333327\n",
      "O=c1c2ccccc2oc2c(Cl)ccc(Cn3ccnc3)c12\tCHEMBL2333326\n",
      "N#Cc1ccc(Cn2ccnc2)c2c(=O)c3ccccc3oc12\tCHEMBL442373\n",
      "O=c1c2ccccc2oc2c(Br)ccc(Cn3ccnc3)c12\tCHEMBL78322\n",
      "O=c1c2ccccc2oc2c([N+](=O)[O-])ccc(Cn3ccnc3)c12\tCHEMBL307160\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL1444\n",
      "Clc1ccc(-n2c(Cn3ccnc3)cc3ccc(Cl)cc32)cc1\tCHEMBL2336855\n",
      "Cc1ccc(-n2c(Cn3ccnc3)cc3ccc(Cl)cc32)cc1\tCHEMBL2336854\n",
      "COc1ccc(-n2c(Cn3ccnc3)cc3ccc(Cl)cc32)cc1\tCHEMBL2336853\n",
      "COc1ccc(-n2c(Cn3ccnc3)cc3ccc(OC)cc32)cc1\tCHEMBL2336852\n",
      "COc1ccc2cc(Cn3ccnc3)n(-c3ccc(C)cc3)c2c1\tCHEMBL2336851\n",
      "COc1ccc2cc(Cn3ccnc3)n(-c3ccc(C#N)cc3)c2c1\tCHEMBL2336850\n",
      "COc1ccc2cc(Cn3ccnc3)n(-c3ccc(C(F)(F)F)cc3)c2c1\tCHEMBL2336849\n",
      "COc1ccc2cc(Cn3ccnc3)n(-c3ccc(F)cc3)c2c1\tCHEMBL2336848\n",
      "COc1ccc2cc(Cn3ccnc3)n(-c3ccccc3)c2c1\tCHEMBL2336847\n",
      "Cc1ccc2cc(Cn3ccnc3)n(-c3ccc(C#N)cc3)c2c1\tCHEMBL2336865\n",
      "Cc1ccc2cc(Cn3ccnc3)n(-c3ccc(F)cc3)c2c1\tCHEMBL2336864\n",
      "COc1ccc(-n2c(Cn3ccnc3)cc3ccc(C)cc32)cc1\tCHEMBL2336863\n",
      "Cc1ccc(-n2c(Cn3ccnc3)cc3ccc(C)cc32)cc1\tCHEMBL2336862\n",
      "Cc1ccc2cc(Cn3ccnc3)n(-c3ccccc3)c2c1\tCHEMBL2336861\n",
      "c1ccc(-n2c(Cn3ccnc3)cc3ccccc32)cc1\tCHEMBL2336860\n",
      "FC(F)(F)c1ccc(-n2c(Cn3ccnc3)cc3ccccc32)cc1\tCHEMBL2336859\n",
      "COc1ccc(-n2c(Cn3ccnc3)cc3ccccc32)cc1\tCHEMBL2336858\n",
      "Cc1ccc(-n2c(Cn3ccnc3)cc3ccccc32)cc1\tCHEMBL2336857\n",
      "Fc1ccc(-n2c(Cn3ccnc3)cc3ccccc32)cc1\tCHEMBL2336856\n",
      "CCC(=C(c1ccc(O)cc1)c1ccc(OCCN(C)C)cc1)c1ccccc1\tCHEMBL2137046\n",
      "CC/C(=C(\\c1ccc(O)cc1)c1ccc(OCCNC)cc1)c1ccccc1\tCHEMBL1093458\n",
      "CC/C(=C(\\c1ccc(O)cc1)c1ccc(OCC(N)=O)cc1)c1ccccc1\tCHEMBL2386286\n",
      "CCC(=C(c1ccc(O)cc1)c1ccc(O)cc1)c1ccccc1\tCHEMBL149791\n",
      "CC/C(=C(\\c1ccc(O)cc1)c1ccc(OCCN)cc1)c1ccccc1\tCHEMBL2386284\n",
      "CC/C(=C(/c1ccc(O)cc1)c1ccc(OCCN)cc1)c1ccccc1\tCHEMBL2386285\n",
      "CCC(=C(c1ccc(O)cc1)c1ccc(OCCN)cc1)c1ccccc1\tCHEMBL3350384\n",
      "CC/C(=C(/c1ccc(O)cc1)c1ccc(OCCN)cc1)c1ccccc1\tCHEMBL2386285\n",
      "CCC(=C(c1ccc(O)cc1)c1ccc(OCCN)cc1)c1ccccc1\tCHEMBL3350384\n",
      "Oc1ccc2ccc(-c3cncc4ccccc34)c(Cl)c2c1\tCHEMBL2420688\n",
      "COc1ccc2ccc(-c3cncc4ccccc34)c(Cl)c2c1\tCHEMBL2420687\n",
      "Oc1ccc2ccc(-c3cncnc3)c(Cl)c2c1\tCHEMBL2420686\n",
      "COc1ccc2ccc(-c3cncnc3)c(Cl)c2c1\tCHEMBL2420685\n",
      "Oc1cncc(-c2ccc3ccc(O)cc3c2Cl)c1\tCHEMBL2420684\n",
      "COc1ccc2ccc(-c3cncc(O)c3)c(Cl)c2c1\tCHEMBL2420683\n",
      "COc1cncc(-c2ccc3ccc(O)cc3c2Cl)c1\tCHEMBL2420682\n",
      "COc1cncc(-c2ccc3ccc(OC)cc3c2Cl)c1\tCHEMBL2420681\n",
      "Oc1ccc2ccc(-c3cncc(F)c3)c(Cl)c2c1\tCHEMBL2420680\n",
      "COc1ccc2ccc(-c3cncc(F)c3)c(Cl)c2c1\tCHEMBL2420679\n",
      "Oc1ccc2ccc(-c3cccnc3)c(Cl)c2c1F\tCHEMBL2420678\n",
      "COc1ccc2ccc(-c3cccnc3)c(Cl)c2c1F\tCHEMBL2420677\n",
      "Oc1ccc2ccc(-c3cccnc3)c(F)c2c1\tCHEMBL2420676\n",
      "COc1ccc2ccc(-c3cccnc3)c(F)c2c1\tCHEMBL2420675\n",
      "Oc1ccc2ccc(-c3cccnc3)c(Cl)c2c1\tCHEMBL2420674\n",
      "COc1ccc2ccc(-c3cccnc3)c(Cl)c2c1\tCHEMBL371430\n",
      "COc1ccc2ccc(-c3cccnc3)cc2c1\tCHEMBL193863\n",
      "c1cncc(-c2ccc3ccccc3c2)c1\tCHEMBL365735\n",
      "O=C1C[C@@H](c2ccc(O)cc2)Oc2cc(O)cc(O)c21\tCHEMBL9352\n",
      "Cc1cn2cc(-c3ccc(O)c(O)c3O)nc2s1\tCHEMBL2430197\n",
      "Oc1ccc(O)c(-c2cn3ccsc3n2)c1\tCHEMBL2430196\n",
      "O=C1C[C@@H](c2ccc(O)cc2)Oc2cc(O)cc(O)c21\tCHEMBL9352\n",
      "C[C@]12C[C@@H](O)[C@H]3[C@@H](CCC4=C(O)C(=O)CC[C@@]43C)[C@@H]1CC[C@@H]2O\tCHEMBL2431527\n",
      "C[C@]12CC[C@H]3[C@@H](CC[C@@H]4C(=O)[C@H](O)CC[C@@]43C)[C@@H]1CC[C@@H]2O\tCHEMBL2431526\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=C(O)C(=O)CC[C@@]43C)[C@@H]1CC[C@@H]2O\tCHEMBL2431525\n",
      "C[C@]12CC[C@H]3[C@@H](CC[C@@]4(O)[C@@H](O)C(=O)CC[C@]34C)[C@@H]1CCC2=O\tCHEMBL2431524\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=C(O)C(=O)CC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL132530\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL1444\n",
      "COc1cccc(C2=CC(=O)c3ccc4c(c3=N2)=CC=N4)c1\tCHEMBL2442766\n",
      "CCn1ccc2c3c(C(=O)O)cc(-c4ccccc4)nc3ccc21\tCHEMBL2442765\n",
      "Cc1cc(C(=O)O)c2ccc3[nH]ccc3c2n1\tCHEMBL2442764\n",
      "COc1ccc(-c2cc(C(=O)O)c3ccc4[nH]ccc4c3n2)cc1\tCHEMBL2442763\n",
      "CCn1ccc2c3c(Cn4cncn4)cc(-c4ccccc4)nc3ccc21\tCHEMBL2442762\n",
      "COc1ccc(-c2cc(Cn3cncn3)c3ccc4[nH]ccc4c3n2)cc1\tCHEMBL2442761\n",
      "CCn1ccc2c3c(Cn4ccnc4)cc(-c4ccccc4)nc3ccc21\tCHEMBL2442760\n",
      "Cc1cc(Cn2ccnc2)c2ccc3[nH]ccc3c2n1\tCHEMBL2442759\n",
      "CCn1ccc2c3nc(-c4ccccc4)cc(Cn4ccnc4)c3ccc21\tCHEMBL2442758\n",
      "COc1ccc(-c2cc(Cn3ccnc3)c3ccc4[nH]ccc4c3n2)cc1\tCHEMBL2442757\n",
      "c1ccc(-c2cc(Cn3ccnc3)c3ccc4[nH]ccc4c3n2)cc1\tCHEMBL2442756\n",
      "CCn1ccc2c3c(CO)cc(-c4ccccc4)nc3ccc21\tCHEMBL2442755\n",
      "Cc1cc(CO)c2ccc3[nH]ccc3c2n1\tCHEMBL2442754\n",
      "CCn1ccc2c3nc(-c4ccccc4)cc(CO)c3ccc21\tCHEMBL2442753\n",
      "COc1ccc(-c2cc(CO)c3ccc4[nH]ccc4c3n2)cc1\tCHEMBL2440145\n",
      "OCc1cc(-c2ccccc2)nc2c1ccc1[nH]ccc12\tCHEMBL2442772\n",
      "CCOC(=O)c1cc(-c2ccccc2)nc2ccc3c(ccn3CC)c12\tCHEMBL2442771\n",
      "CCOC(=O)c1cc(C)nc2c1ccc1[nH]ccc12\tCHEMBL2442770\n",
      "CCOC(=O)c1cc(-c2ccccc2)nc2c1ccc1c2ccn1CC\tCHEMBL2442769\n",
      "CCOC(=O)c1cc(-c2ccc(OC)cc2)nc2c1ccc1[nH]ccc12\tCHEMBL2442768\n",
      "CCOC(=O)c1cc(-c2ccccc2)nc2c1ccc1[nH]ccc12\tCHEMBL2442767\n",
      "O=C1/C(=C\\c2ccncc2)C(c2ccccc2)n2cccc21\tCHEMBL3099735\n",
      "O=C1/C(=C\\c2cccnc2)C(c2ccc(OC(F)(F)F)cc2)n2cccc21\tCHEMBL1095413\n",
      "O=C1/C(=C\\c2cccnc2)C(c2ccccc2)n2cccc21\tCHEMBL3099734\n",
      "N#Cc1ccc([C@H]2CCc3cncn32)c(F)c1\tCHEMBL3099695\n",
      "COC(=O)[C@]1(c2ccc(C#N)cc2Cl)CCc2cncn21\tCHEMBL3099686\n",
      "N#Cc1ccc([C@H]2CCCCc3cncn32)c(F)c1\tCHEMBL3099683\n",
      "N#Cc1ccc([C@H]2CCCc3cncn32)cc1\tCHEMBL287677\n",
      "O=C1c2ccccc2OCC1c1cccnc1\tCHEMBL2011186\n",
      "O=C1c2ccccc2OCC1c1ccc(Oc2ccccc2)cc1\tCHEMBL2011177\n",
      "O=C1CC(c2ccccc2)Oc2ccccc21\tCHEMBL274318\n",
      "O=c1cc(-c2ccccc2)oc2ccccc12\tCHEMBL275638\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL1444\n",
      "O=C1c2cc(F)ccc2OCC1c1cccnc1\tCHEMBL3105528\n",
      "O=C1c2cccc(F)c2OCC1c1cccnc1\tCHEMBL3105527\n",
      "COc1ccc2c(c1)C(=O)C(c1cccnc1)CO2\tCHEMBL3105526\n",
      "O=C1c2c(ccc3ccccc23)OCC1c1ccc(Oc2ccccc2)cc1\tCHEMBL3105524\n",
      "O=C1c2ccccc2OCC1c1cc(F)cc(F)c1\tCHEMBL3105522\n",
      "O=C1c2ccccc2OCC1c1ccc(F)cc1\tCHEMBL3105521\n",
      "O=C1c2ccccc2OCC1c1cccc(F)c1\tCHEMBL3105520\n",
      "O=C1c2ccccc2OCC1c1ccccc1F\tCHEMBL3105519\n",
      "O=C1c2cccc(F)c2OCC1c1ccccc1\tCHEMBL3105517\n",
      "COc1cc(O)c([C@H]2C[C@@H](c3ccccc3)Oc3c(C=O)c(O)cc(O)c32)c(O)c1C(=O)/C=C/c1ccccc1\tCHEMBL3134108\n",
      "COc1cc(O)c([C@@H]2C[C@@H](c3ccccc3)Oc3c(C=O)c(O)cc(O)c32)c(O)c1C(=O)/C=C/c1ccccc1\tCHEMBL3134107\n",
      "O=C1C[C@@H](c2ccccc2)Oc2cc(O)cc(O)c21\tCHEMBL399910\n",
      "O=c1cc(-c2ccccc2)oc2cc(O)cc(O)c12\tCHEMBL117\n",
      "COc1c(C)c(O)c2c(c1C)O[C@H](c1ccccc1)CC2=O\tCHEMBL53343\n",
      "COc1c(C)c(OC)c2c(c1C)O[C@H](c1ccccc1)C[C@H]2O\tCHEMBL3134111\n",
      "COc1cc(O)c2c(c1C)O[C@H](c1ccccc1)CC2=O\tCHEMBL3134115\n",
      "COc1c(C)c(OC)c2c(c1C=O)O[C@H](c1ccccc1)C[C@H]2O\tCHEMBL3134110\n",
      "COc1c(C)c(O)c(C=O)c2c1[C@H](OC)C[C@@H](c1ccccc1)O2\tCHEMBL3134114\n",
      "COc1cc(O)c(C=O)c2c1[C@H](OC)C[C@@H](c1ccccc1)O2\tCHEMBL3134112\n",
      "COc1cc(O)c(C=O)c2c1[C@H](O)C[C@@H](c1ccccc1)O2\tCHEMBL3134109\n",
      "COc1c(C)c(O)c(C=O)c2c1[C@H](O)C[C@@H](c1ccccc1)O2\tCHEMBL3134113\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL1444\n",
      "COc1ccc2c(c1)N[C@H]1c3ccc(O)cc3OC[C@@H]21\tCHEMBL3134228\n",
      "Cc1cc(O)cc2c1Oc1cc(O)cc(C)c1C(=O)O2\tCHEMBL3221175\n",
      "O=c1c2ccccc2[nH]c2c(Cn3ccnc3)cccc12\tCHEMBL3219070\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL1444\n",
      "O=c1c2ccccc2oc2c(Cn3ccnc3)ccc([N+](=O)[O-])c12\tCHEMBL159073\n",
      "O=c1c2ccccc2sc2c(Cn3ccnc3)ccc([N+](=O)[O-])c12\tCHEMBL1083665\n",
      "COc1ccc2c(c1)C(=O)/C(=C/c1c[nH]cn1)CC2\tCHEMBL3219600\n",
      "O=C1C[C@@H](c2ccc(O)c(O)c2)Oc2cc(O)ccc21\tCHEMBL451168\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=C(O)C(=O)CC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL132530\n",
      "CC(C)(C(=O)c1cccnc1)c1cccnc1\tCHEMBL934\n",
      "O=C(Cn1ccnc1)c1ccc(OCc2cccnc2)cc1\tCHEMBL3359965\n",
      "O=C(Cn1ccnc1)c1ccc(OCc2ccc(OC(F)(F)F)cc2)cc1\tCHEMBL3359964\n",
      "N#Cc1cccc(COc2ccc(C(=O)Cn3ccnc3)cc2)c1\tCHEMBL3359963\n",
      "O=C(Cn1ccnc1)c1ccc(OCc2cccc([N+](=O)[O-])c2)cc1\tCHEMBL3359962\n",
      "O=C(Cn1ccnc1)c1ccc(OCc2cccc(OC(F)(F)F)c2)cc1\tCHEMBL3359961\n",
      "O=C(Cn1ccnc1)c1ccc(OCc2ccccc2)cc1\tCHEMBL3359960\n",
      "O=C(Cn1ccnc1)c1cccc(OCc2ccccc2)c1\tCHEMBL3359959\n",
      "COc1cc(COc2ccc3c(Cn4ccnc4)cc(=O)oc3c2)cc(OC)c1OC\tCHEMBL3359958\n",
      "COc1ccc(COc2ccc3c(Cn4ccnc4)cc(=O)oc3c2)cc1OC\tCHEMBL3359957\n",
      "O=c1cc(Cn2ccnc2)c2ccc(OCc3ccc(F)c(F)c3)cc2o1\tCHEMBL1688901\n",
      "O=c1cc(Cn2ccnc2)c2ccc(OCc3cc(F)cc(F)c3)cc2o1\tCHEMBL1688900\n",
      "O=c1cc(Cn2ccnc2)c2ccc(OCCc3ccccc3)cc2o1\tCHEMBL3359956\n",
      "CC(C)Oc1ccc(COc2ccc3c(Cn4ccnc4)cc(=O)oc3c2)cc1\tCHEMBL3359955\n",
      "CCCOc1ccc(COc2ccc3c(Cn4ccnc4)cc(=O)oc3c2)cc1\tCHEMBL3359954\n",
      "COc1ccc(COc2ccc3c(Cn4ccnc4)cc(=O)oc3c2)cc1\tCHEMBL1688898\n",
      "O=c1cc(Cn2ccnc2)c2ccc(OCc3ccc([N+](=O)[O-])cc3)cc2o1\tCHEMBL3359953\n",
      "O=c1cc(Cn2ccnc2)c2ccc(OCc3ccc(OC(F)(F)F)cc3)cc2o1\tCHEMBL1688899\n",
      "O=c1cc(Cn2ccnc2)c2ccc(OCc3ccc(Cl)cc3)cc2o1\tCHEMBL1688897\n",
      "COc1cccc(COc2ccc3c(Cn4ccnc4)cc(=O)oc3c2)c1\tCHEMBL3359952\n",
      "O=c1cc(Cn2ccnc2)c2ccc(OCc3cccc([N+](=O)[O-])c3)cc2o1\tCHEMBL1688895\n",
      "O=c1cc(Cn2ccnc2)c2ccc(OCc3cccc(OC(F)(F)F)c3)cc2o1\tCHEMBL1688894\n",
      "O=c1cc(Cn2ccnc2)c2ccc(OCc3cccc(C(F)(F)F)c3)cc2o1\tCHEMBL1688892\n",
      "O=c1cc(Cn2ccnc2)c2ccc(OCc3cccc(Cl)c3)cc2o1\tCHEMBL1688890\n",
      "O=c1cc(Cn2ccnc2)c2ccc(OCc3cccc(F)c3)cc2o1\tCHEMBL1688891\n",
      "Cc1cccc(COc2ccc3c(Cn4ccnc4)cc(=O)oc3c2)c1\tCHEMBL1688893\n",
      "O=c1cc(Cn2ccnc2)c2ccc(OCc3ccccc3)cc2o1\tCHEMBL225499\n",
      "O=c1cc(Cn2ccnc2)c2ccc(Oc3ccccc3)cc2o1\tCHEMBL437731\n",
      "N#Cc1ccc([C@H]2CCc3cncn32)c(F)c1\tCHEMBL3099695\n",
      "N#Cc1ccc(C2CCCc3cncn32)cc1\tCHEMBL9298\n",
      "CC1=C(O)C(=O)C[C@]2(C)[C@H]3CCc4cocc4[C@]3(CO)CCC12O\tCHEMBL3315157\n",
      "O=C1C[C@@H](c2ccc(O)cc2)Oc2cc(O)cc(O)c21\tCHEMBL9352\n",
      "CC(C)=CCc1cc2cc(-c3cc(O)cc(O)c3)oc2cc1O\tCHEMBL465881\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "N#Cc1ccc(C2CCCc3cncn32)cc1\tCHEMBL9298\n",
      "C=C1C[C@@H]2[C@H](CC[C@]3(C)C(=O)CC[C@@H]23)[C@@]2(C)C=CC(=O)C=C12\tCHEMBL1200374\n",
      "C[C@]12C=CC(=O)C=C1C(CO)=C[C@@H]1[C@@H]2CC[C@]2(C)C(=O)CC[C@@H]12\tCHEMBL3407539\n",
      "C=C1C[C@@H]2[C@H](CC[C@]3(C)[C@@H](O)CC[C@@H]23)[C@@]2(C)C=CC(=O)C=C12\tCHEMBL3407538\n",
      "C=C1C[C@H]2[C@@H]3CCC(=O)[C@@]3(C)CC[C@@H]2[C@]2(C)C1=CC(=O)[C@@H]1O[C@@H]12\tCHEMBL3407537\n",
      "C[C@]12C=CC(=O)C=C1[C@@]1(CO1)C[C@@H]1[C@@H]2CC[C@]2(C)C(=O)CC[C@@H]12\tCHEMBL3407536\n",
      "C=C1C[C@@H]2[C@H](CC[C@]3(C)C(=O)CC[C@@H]23)[C@@]2(C)C=CC(=O)C=C12\tCHEMBL1200374\n",
      "C[C@]12C=CC(=O)C=C1C(CO)=C[C@@H]1[C@@H]2CC[C@]2(C)C(=O)CC[C@@H]12\tCHEMBL3407539\n",
      "C=C1C[C@@H]2[C@H](CC[C@]3(C)[C@@H](O)CC[C@@H]23)[C@@]2(C)C=CC(=O)C=C12\tCHEMBL3407538\n",
      "C=C1C[C@H]2[C@@H]3CCC(=O)[C@@]3(C)CC[C@@H]2[C@]2(C)C1=CC(=O)[C@@H]1O[C@@H]12\tCHEMBL3407537\n",
      "C[C@]12C=CC(=O)C=C1[C@@]1(CO1)C[C@@H]1[C@@H]2CC[C@]2(C)C(=O)CC[C@@H]12\tCHEMBL3407536\n",
      "Fc1cc2c(cc1-c1cccnc1)CCc1nncn1-2\tCHEMBL3415152\n",
      "Fc1cc(-c2cccnc2)cc2c1-n1cnnc1CC2\tCHEMBL3415153\n",
      "Cc1nnc2n1-c1cc(F)c(-c3cccnc3)cc1CC2\tCHEMBL3415154\n",
      "Cc1nnc2n1-c1c(F)cc(-c3cccnc3)cc1CC2\tCHEMBL3415155\n",
      "CCc1nnc2n1-c1cc(F)c(-c3cccnc3)cc1CC2\tCHEMBL3415156\n",
      "CCc1nnc2n1-c1c(F)cc(-c3cccnc3)cc1CC2\tCHEMBL3415157\n",
      "CC(C)c1nnc2n1-c1cc(F)c(-c3cccnc3)cc1CC2\tCHEMBL3415158\n",
      "CC(C)c1nnc2n1-c1c(F)cc(-c3cccnc3)cc1CC2\tCHEMBL3415159\n",
      "Fc1cc2c(cc1-c1cccnc1)CCc1nnc(-c3ccccc3)n1-2\tCHEMBL3415160\n",
      "Fc1cc(-c2cccnc2)cc2c1-n1c(nnc1-c1ccccc1)CC2\tCHEMBL3415161\n",
      "Fc1cc2c(cc1-c1cccnc1)CCc1nnc(Cc3ccccc3)n1-2\tCHEMBL3415162\n",
      "O=C(c1ccccc1)c1nnc2n1-c1c(F)cc(-c3cccnc3)cc1CC2\tCHEMBL3415163\n",
      "Fc1cc2c(cc1-c1cccnc1)CCc1nnc(-c3ccncc3)n1-2\tCHEMBL3415164\n",
      "Fc1cc(-c2cccnc2)cc2c1-n1c(nnc1-c1ccncc1)CC2\tCHEMBL3415165\n",
      "COc1cncc(-c2cc(F)c3c(c2)CCc2nnc(C)n2-3)c1\tCHEMBL3415166\n",
      "Cc1nnc2n1-c1c(F)cc(-c3cncc(C(C)O)c3)cc1CC2\tCHEMBL3415167\n",
      "CC(=O)c1cncc(-c2cc(F)c3c(c2)CCc2nnc(C)n2-3)c1\tCHEMBL3415168\n",
      "Cc1nnc2n1-c1c(F)cc(-c3cncc(C(F)(F)F)c3)cc1CC2\tCHEMBL3415169\n",
      "Cc1nnc2n1-c1c(F)cc(-c3cncc(C#N)c3)cc1CC2\tCHEMBL3415170\n",
      "Cc1nnc2n1-c1c(F)cc(-c3cncc(F)c3)cc1CC2\tCHEMBL2331699\n",
      "Cc1nnc2n1-c1c(F)cc(-c3cncc(-c4ccccc4)c3)cc1CC2\tCHEMBL3415171\n",
      "Cc1nnc2n1-c1c(F)cc(-c3cncc4ccccc34)cc1CC2\tCHEMBL3415172\n",
      "Cc1ccncc1-c1cc(F)c2c(c1)CCc1nnc(C)n1-2\tCHEMBL3313969\n",
      "Cc1c(F)cncc1-c1cc(F)c2c(c1)CCc1nnc(C)n1-2\tCHEMBL3415173\n",
      "Fc1cncc(-c2cc(F)c3c(c2)CCc2nncn2-3)c1\tCHEMBL3415174\n",
      "Cc1c(F)cncc1-c1cc(F)c2c(c1)CCc1nncn1-2\tCHEMBL3415175\n",
      "c1ccc(-c2ccc(Cn3ccnc3)cn2)cc1\tCHEMBL428780\n",
      "Cc1cncc(Cc2ccc(-c3ccccc3)nc2)c1\tCHEMBL2417553\n",
      "O=C1CCc2cc(-c3cccnc3)c(OCc3ccccc3)cc2N1\tCHEMBL2165320\n",
      "CC(C)(C(=O)c1cccnc1)c1cccnc1\tCHEMBL934\n",
      "O=[N+]([O-])c1ccc(-c2cccnc2)c(OCc2ccccc2)c1\tCHEMBL3421765\n",
      "O=[N+]([O-])c1ccc(-c2cccnc2)c(OCCc2ccccc2)c1\tCHEMBL3421766\n",
      "O=[N+]([O-])c1ccc(-c2cccnc2)c(OCCCc2ccccc2)c1\tCHEMBL3421767\n",
      "O=[N+]([O-])c1ccc(-c2ccncc2)c(OCc2ccccc2)c1\tCHEMBL3421768\n",
      "O=[N+]([O-])c1ccc(-c2ccncc2)c(OCCc2ccccc2)c1\tCHEMBL3421769\n",
      "O=[N+]([O-])c1ccc(-c2ccncc2)c(OCCCc2ccccc2)c1\tCHEMBL3421770\n",
      "FC(F)(F)c1ccc(-c2ccncc2)c(OCCc2ccccc2)c1\tCHEMBL3421778\n",
      "N#Cc1ccc(-c2cccnc2)c(OCCc2ccccc2)c1\tCHEMBL3421779\n",
      "N#Cc1ccc(-c2ccncc2)c(OCCc2ccccc2)c1\tCHEMBL3421780\n",
      "O=[N+]([O-])c1ccc(Cc2cccnc2)c(OCCc2ccccc2)c1\tCHEMBL3421785\n",
      "O=[N+]([O-])c1ccc(Cc2ccncc2)c(OCCc2ccccc2)c1\tCHEMBL3421786\n",
      "Nc1ccc(-c2cccnc2)c(OCc2ccccc2)c1\tCHEMBL3421764\n",
      "NCCOc1ccc(/C(=C(\\Cn2cnnc2)c2ccccc2)c2ccc(O)cc2)cc1\tCHEMBL3422044\n",
      "NCCOc1ccc(/C(=C(/Cn2cnnc2)c2ccccc2)c2ccc(O)cc2)cc1\tCHEMBL3422043\n",
      "NCCOc1ccc(/C(=C(\\Cn2cncn2)c2ccccc2)c2ccc(O)cc2)cc1\tCHEMBL3422042\n",
      "NCCOc1ccc(/C(=C(\\Cn2ccnc2)c2ccccc2)c2ccc(O)cc2)cc1\tCHEMBL3422041\n",
      "N#CC/C(=C(/c1ccc(O)cc1)c1ccc(OCCN)cc1)c1ccccc1\tCHEMBL3422040\n",
      "NCCOc1ccc(C(=C(c2ccccc2)c2ccccc2)c2ccc(O)cc2)cc1\tCHEMBL3422039\n",
      "CC(C)/C(=C(/c1ccc(O)cc1)c1ccc(OCCN)cc1)c1ccccc1\tCHEMBL3422038\n",
      "CCC/C(=C(/c1ccc(O)cc1)c1ccc(OCCN)cc1)c1ccccc1\tCHEMBL3422037\n",
      "NCCOc1ccc(/C(=C(\\CCCl)c2ccccc2)c2ccc(O)cc2)cc1\tCHEMBL3422036\n",
      "C/C(=C(/c1ccc(O)cc1)c1ccc(OCCN)cc1)c1ccccc1\tCHEMBL3422035\n",
      "NCCOc1ccc(/C(=C/c2ccccc2)c2ccc(O)cc2)cc1\tCHEMBL3422034\n",
      "CC/C(=C(/c1ccc(N)cc1)c1ccc(OCCN)cc1)c1ccc(O)cc1\tCHEMBL3422033\n",
      "CC/C(=C(/c1ccc(N)cc1)c1ccc(OCCN)cc1)c1ccccc1\tCHEMBL3422032\n",
      "CC/C(=C(/c1ccccc1)c1ccc(OCCN)cc1)c1ccccc1\tCHEMBL3422031\n",
      "CCC(CC)=C(c1ccc(O)cc1)c1ccc(OCCN)cc1\tCHEMBL3422030\n",
      "CC/C(=C(/c1ccc(O)cc1)c1ccc(OCCN)cc1)c1ccc(F)cc1\tCHEMBL3422029\n",
      "CC/C(=C(/c1ccc(O)cc1)c1ccc(OCCN)cc1)c1ccc(O)cc1\tCHEMBL3422028\n",
      "NCCOc1ccc(/C(=C2\\CCc3ccccc32)c2ccc(O)cc2)cc1\tCHEMBL3422045\n",
      "CC/C(=C(/c1ccc(O)cc1)c1ccc(OCCO)cc1)c1ccccc1\tCHEMBL3422046\n",
      "CC/C(=C(/c1ccc(O)cc1)c1ccc(OCC(=O)O)cc1)c1ccccc1\tCHEMBL3422047\n",
      "CC/C(=C(/c1ccc(O)cc1)c1ccc(OCCCN)cc1)c1ccccc1\tCHEMBL3422050\n",
      "CC/C(=C(/c1ccc(O)cc1)c1ccc(CCCN)cc1)c1ccccc1\tCHEMBL3422051\n",
      "CC/C(=C(/c1ccc(O)cc1)c1ccc(CCCN)cc1)c1ccc(O)cc1\tCHEMBL3422052\n",
      "CC/C(=C(/c1ccc(O)cc1)c1ccc(OCCN)cc1)c1ccccc1\tCHEMBL2386285\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL1444\n",
      "Fc1cncc(-c2nc3cc(F)c(F)cc3n2C2CC2)c1\tCHEMBL3582472\n",
      "CC(C)(O)c1cncc(-c2nc3cc(F)c(F)cc3n2C2CC2)c1\tCHEMBL3582477\n",
      "CC(C)(O)c1cncc(-c2nc3ccc(F)cc3n2C2CC2)c1\tCHEMBL3582478\n",
      "O=S(=O)(NCCc1ccccc1)c1ccc(-n2cc(-c3ccccc3)nn2)cc1\tCHEMBL2430505\n",
      "O=S(=O)(NCCc1ccccc1)c1ccc(-n2cc(COc3ccccc3)nn2)cc1\tCHEMBL2430506\n",
      "Cc1ccc(OCc2cn(-c3ccc(S(=O)(=O)NCCc4ccccc4)cc3)nn2)cc1\tCHEMBL2430509\n",
      "O=Cc1ccc(OCc2cn(-c3ccc(S(=O)(=O)NCCc4ccccc4)cc3)nn2)cc1\tCHEMBL2430510\n",
      "O=[N+]([O-])c1ccc(OCc2cn(-c3ccc(S(=O)(=O)NCCc4ccccc4)cc3)nn2)cc1\tCHEMBL2430511\n",
      "Cc1ccccc1OCc1cn(-c2ccc(S(=O)(=O)NCCc3ccccc3)cc2)nn1\tCHEMBL2430512\n",
      "O=S(=O)(NCCc1ccccc1)c1ccc(-n2cc(COc3cccc4ccccc34)nn2)cc1\tCHEMBL2430507\n",
      "O=S(=O)(NCCc1ccccc1)c1ccc(-n2cc(COc3ccc4ccccc4c3)nn2)cc1\tCHEMBL2430508\n",
      "O=c1ccc2ccc(OCc3cn(-c4ccc(S(=O)(=O)NCCc5ccccc5)cc4)nn3)cc2o1\tCHEMBL2430497\n",
      "O=c1cc(OCc2cn(-c3ccc(S(=O)(=O)NCCc4ccccc4)cc3)nn2)c2ccccc2o1\tCHEMBL2430498\n",
      "COc1ccc(CCNS(=O)(=O)c2ccc(-n3cc(-c4ccccc4)nn3)cc2)cc1OC\tCHEMBL2430499\n",
      "COc1ccc(CCNS(=O)(=O)c2ccc(-n3cc(COc4ccc([N+](=O)[O-])cc4)nn3)cc2)cc1OC\tCHEMBL2430501\n",
      "CC(=O)c1ccccc1OCc1cn(-c2ccc(S(=O)(=O)N3CCc4ccccc4C3)cc2)nn1\tCHEMBL3264672\n",
      "O=c1cc(OCc2cn(-c3ccc(S(=O)(=O)N4CCc5ccccc5C4)cc3)nn2)c2ccccc2o1\tCHEMBL3264675\n",
      "COc1cc2c(cc1OC)CN(S(=O)(=O)c1ccc(-n3cc(COc4ccc([N+](=O)[O-])cc4)nn3)cc1)CC2\tCHEMBL3264684\n",
      "COc1cc2c(cc1OC)CN(S(=O)(=O)c1ccc(-n3cc(COc4ccccc4C)nn3)cc1)CC2\tCHEMBL3264679\n",
      "COC(=O)c1ccccc1OCc1cn(-c2ccc(S(=O)(=O)N3CCc4cc(OC)c(OC)cc4C3)cc2)nn1\tCHEMBL3264682\n",
      "COc1cc2c(cc1OC)CN(S(=O)(=O)c1ccc(-n3cc(COc4ccccc4C(C)=O)nn3)cc1)CC2\tCHEMBL3264681\n",
      "COc1cc2c(cc1OC)CN(S(=O)(=O)c1ccc(-n3cc(COc4cc(C=O)ccc4OC)nn3)cc1)CC2\tCHEMBL3264685\n",
      "O=c1cc(OCc2cn(-c3cccc(S(=O)(=O)N4CCc5ccccc5C4)c3)nn2)c2ccccc2o1\tCHEMBL3589964\n",
      "COc1cc2c(cc1OC)CN(S(=O)(=O)c1cccc(-n3cc(COc4ccc5ccc(=O)oc5c4)nn3)c1)CC2\tCHEMBL3589965\n",
      "COc1cc2c(cc1OC)CN(S(=O)(=O)c1cccc(-n3cc(COc4cc(=O)oc5ccccc45)nn3)c1)CC2\tCHEMBL3589966\n",
      "CC(=O)N1CCN(c2ccc(OCC3COC(Cn4ccnc4)(c4ccc(Cl)cc4Cl)O3)cc2)CC1\tCHEMBL157101\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL1444\n",
      "N#Cc1ccc(N(Cc2ccc(OS(N)(=O)=O)cc2)n2cnnc2)cc1\tCHEMBL430879\n",
      "N#Cc1ccc(N(CCSc2ccc(O)cc2)n2cnnc2)cc1\tCHEMBL3622054\n",
      "N#Cc1ccc(N(CCSc2ccc(O)c(Cl)c2)n2cnnc2)cc1\tCHEMBL3622055\n",
      "N#Cc1ccc(N(CCSc2ccc(OS(N)(=O)=O)cc2)n2cnnc2)cc1\tCHEMBL3622056\n",
      "N#Cc1ccc(N(CCSc2ccc(OS(N)(=O)=O)c(Cl)c2)n2cnnc2)cc1\tCHEMBL3622057\n",
      "N#Cc1ccc(N(CCCSc2ccc(OS(N)(=O)=O)cc2)n2cnnc2)cc1\tCHEMBL3622058\n",
      "N#Cc1ccc(N(CCCCCSc2ccc(OS(N)(=O)=O)cc2)n2cnnc2)cc1\tCHEMBL3622059\n",
      "N#Cc1ccc(N(CCCS(=O)(=O)c2ccc(O)cc2)n2cnnc2)cc1\tCHEMBL3622060\n",
      "N#Cc1ccc(N(CC2CCc3cc(OS(N)(=O)=O)ccc3C2)n2cnnc2)cc1\tCHEMBL3622061\n",
      "N#Cc1ccc(N(Cc2ccc3cc(OS(N)(=O)=O)ccc3c2)n2cnnc2)cc1\tCHEMBL3622062\n",
      "N#Cc1ccc(N(Cc2ccc3c(Cl)c(OS(N)(=O)=O)ccc3c2)n2cnnc2)cc1\tCHEMBL3622063\n",
      "N#Cc1ccc(N(Cc2ccc3c(Br)c(OS(N)(=O)=O)ccc3c2)n2cnnc2)cc1\tCHEMBL3622064\n",
      "N#Cc1ccc(N(CCn2ccc3cc(OS(N)(=O)=O)ccc32)n2cnnc2)cc1\tCHEMBL3622065\n",
      "Cn1cc(CCN(c2ccc(C#N)cc2)n2cnnc2)c2cc(OS(N)(=O)=O)ccc21\tCHEMBL3622066\n",
      "N#Cc1ccc(N(CCc2coc3ccc(OS(N)(=O)=O)cc23)n2cnnc2)cc1\tCHEMBL3622067\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL1444\n",
      "NS(=O)(=O)Oc1ccc(C(c2ccc(OS(N)(=O)=O)cc2)n2cncn2)cc1\tCHEMBL479942\n",
      "N#Cc1ccc(C(c2ccc(OS(N)(=O)=O)c(Br)c2)n2cncn2)cc1\tCHEMBL518761\n",
      "N#Cc1ccc([C@H](c2ccc(OS(N)(=O)=O)c(Br)c2)n2cncn2)cc1\tCHEMBL479559\n",
      "N#Cc1ccc([C@@H](c2ccc(OS(N)(=O)=O)c(Br)c2)n2cncn2)cc1\tCHEMBL480321\n",
      "Cn1nnc2ccc([C@H](c3ccc(Cl)cc3)n3cncn3)cc21\tCHEMBL224060\n",
      "Cn1nnc2ccc(C(c3ccc(OS(N)(=O)=O)cc3)n3cncn3)cc21\tCHEMBL3622363\n",
      "N#Cc1ccc(Cn2cncn2)cc1-c1ccc(OS(N)(=O)=O)c(Cl)c1\tCHEMBL592131\n",
      "N#Cc1ccc(Cn2cncn2)cc1-c1ccc(O)c(Cl)c1\tCHEMBL597808\n",
      "CC(C)(C#N)c1cc(Cn2cncn2)cc(C(C)(C)C#N)c1\tCHEMBL1399\n",
      "CC(C)(C#N)c1cc(Cn2cncn2)cc(Sc2ccccc2)c1\tCHEMBL3622069\n",
      "CC(C)(C#N)c1cc(Cn2cncn2)cc(Sc2ccc(OS(N)(=O)=O)cc2)c1\tCHEMBL3622070\n",
      "CC(C)(C#N)c1cc(Cn2cncn2)cc(Sc2ccc(OS(N)(=O)=O)c(Cl)c2)c1\tCHEMBL3622071\n",
      "CC(C)(C#N)c1cc(Cn2cncn2)cc(Sc2ccc(OS(N)(=O)=O)c(Br)c2)c1\tCHEMBL3622072\n",
      "N#CC1(c2cc(Cn3cncn3)cc(Sc3ccc(OS(N)(=O)=O)cc3)c2)CCC1\tCHEMBL3622073\n",
      "N#CC1(c2cc(Cn3cncn3)cc(Sc3ccc(OS(N)(=O)=O)cc3)c2)CC1\tCHEMBL3622075\n",
      "CC(C)(C#N)c1cc(Cn2cncn2)cc(Oc2ccc(OS(N)(=O)=O)cc2)c1\tCHEMBL3622076\n",
      "CC(C)(C#N)c1cc(/C=C/c2ccc(OS(N)(=O)=O)cc2)cc(Cn2cncn2)c1\tCHEMBL3622077\n",
      "N#Cc1ccc(N(Cc2ccc(Br)cc2)n2cnnc2)cc1\tCHEMBL108425\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=CC(=O)[C@H](F)C[C@@]43C)[C@@H]1CCC2=O\tCHEMBL3623218\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=C(Sc5ccccc5)C(=O)CC[C@@]43C)[C@@H]1C[C@H](C#N)[C@@H]2O\tCHEMBL3623217\n",
      "C[C@]12CC[C@H]3[C@@H](CC[C@H]4C(=O)C(O)=C(C#N)C[C@]34C)[C@@H]1CC[C@@H]2O\tCHEMBL3623216\n",
      "C[C@]12CC[C@H]3[C@@H](CC[C@H]4C(=O)c5[nH]ncc5C[C@]34C)[C@@H]1CC[C@@H]2O\tCHEMBL3623215\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=CC(=O)CC[C@@]43C)[C@@H]1C/C(=C\\c1cccc(OCCCn3ccnc3)c1)C2=O\tCHEMBL2282050\n",
      "C[C@]12C=CC(=O)C=C1CC[C@@H]1[C@@H]2CC[C@]2(C)C(=O)[C@@H](n3ccnc3)C[C@@H]12\tCHEMBL3623214\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=CC(=O)CC[C@@]43C)[C@@H]1C[C@H](n1ccnc1)C2=O\tCHEMBL3623213\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=CC(=O)CC[C@@]43C)[C@@H]1C/C(=C\\c1ccncc1)C2=O\tCHEMBL2003274\n",
      "CC(=O)[C@@]1(C)CC[C@H]2[C@@H](C=CC3=CC(=O)CC[C@@]32C)[C@@H]1CC#N\tCHEMBL3621226\n",
      "C[C@@H]1OC(=O)C[C@H]2[C@@H]3CC=C4CC(O)CC[C@]4(C)[C@H]3CC[C@]12C\tCHEMBL3623209\n",
      "CC(=O)[C@@]1(C)CC[C@H]2[C@@H](CCC3=CC(=O)CC[C@@]32C)[C@@H]1CC#N\tCHEMBL3623208\n",
      "CC[C@@H]1C[C@@]2(C)C(=CC1=O)CC[C@H]1[C@@H]3CCC(=O)[C@@]3(C)CC[C@@H]12\tCHEMBL3623219\n",
      "C[C@]12CC[C@H]3[C@@H](C[C@H]4OC[C@@]35CCC(=O)C=C45)[C@@H]1CCC2=O\tCHEMBL3623222\n",
      "C=CC[C@@H]1CC2=CC(=O)C=C[C@]2(C)[C@H]2CC[C@]3(C)C(=O)CC[C@H]3[C@@H]21\tCHEMBL3623223\n",
      "CCC#CCO[C@@H]1C[C@@H]2[C@H](CC[C@]3(C)C(=O)CC[C@@H]23)[C@@]2(C)C=CC(=O)C=C12\tCHEMBL2179110\n",
      "COc1ccc2c(c1)C(=O)/C(=C/c1c[nH]cn1)CC2\tCHEMBL3219600\n",
      "C1=C(CCn2ccnc2)c2cccnc2CC1\tCHEMBL293122\n",
      "CCn1cc(C(c2ccccc2)n2ccnc2)c2ccccc21\tCHEMBL3623225\n",
      "CCn1ccc2cc(C(c3ccc(F)cc3)n3ccnc3)ccc21\tCHEMBL41761\n",
      "CCc1nc(C)cn1Cc1ccccc1CC(=O)O\tCHEMBL3623229\n",
      "c1ccc(Cn2c3ccccc3c3ccccc32)cc1\tCHEMBL3623230\n",
      "O=c1cc(-c2ccccc2)oc2cc(O)cc(O)c12\tCHEMBL117\n",
      "O=c1cc(-c2ccc(O)cc2)oc2cc(O)cc(O)c12\tCHEMBL28\n",
      "O=c1cc(-c2ccccc2)oc2ccccc12\tCHEMBL275638\n",
      "O=C1CC(c2ccccc2)Oc2ccccc21\tCHEMBL274318\n",
      "O=c1c(O)c(-c2ccc(O)c(O)c2)oc2cc(O)cc(O)c12\tCHEMBL50\n",
      "COc1ccc(-c2coc3cc(O)cc(O)c3c2=O)cc1\tCHEMBL131921\n",
      "COc1ccc2c(c1)C1C(CC2)C1c1ccncc1\tCHEMBL294361\n",
      "Fc1ccc(C(c2cc3ccccc3o2)n2cncn2)cc1\tCHEMBL3623226\n",
      "COc1ccc2c(c1)C1C(CC2)C1c1ccncc1\tCHEMBL294361\n",
      "O=c1c(-c2ccccc2)c(SCc2ccncc2)oc2cc(OCc3ccccc3)ccc12\tCHEMBL188664\n",
      "Fc1ccc2c(c1)OC(c1ccccc1)CC2n1ccnc1\tCHEMBL3623227\n",
      "COc1cc(C2CC(=O)Oc3cc4c(cc32)OCO4)cc(OC)c1O\tCHEMBL3623228\n",
      "N#Cc1ccc(Cn2ccnc2)c2oc3ccccc3c(=O)c12\tCHEMBL421149\n",
      "O=c1c2ccccc2oc2c(Cn3ccnc3)ccc([N+](=O)[O-])c12\tCHEMBL159073\n",
      "O=c1cc(Cn2ccnc2)c2ccc(Oc3ccccc3)cc2o1\tCHEMBL437731\n",
      "NS(=O)(=O)Oc1ccc(C(c2ccc(OS(N)(=O)=O)cc2)n2cncn2)cc1\tCHEMBL479942\n",
      "N#Cc1ccc(C(c2ccc(OS(N)(=O)=O)c(Br)c2)n2cncn2)cc1\tCHEMBL518761\n",
      "N#Cc1ccc(C(CCc2ccc(OS(N)(=O)=O)c(Br)c2)n2cncn2)cc1\tCHEMBL3623231\n",
      "N#Cc1ccc(N(Cc2ccc(OS(N)(=O)=O)cc2F)n2ccnc2)cc1\tCHEMBL3623232\n",
      "COc1cc(/C=C2\\C[C@H]3[C@@H]4CC=C5C[C@@H](O)CC[C@]5(C)[C@H]4CC[C@]3(C)[C@H]2O)ccc1OCCCn1ccnc1\tCHEMBL3623212\n",
      "C[C@]12CC[C@@H]3c4cc(NC(=O)c5ccncc5)c(O)cc4CC[C@H]3[C@@H]1CCC2=O\tCHEMBL3623220\n",
      "C[C@]12CC[C@@H]3c4ccc(O)c(NC(=O)c5ccncc5)c4CC[C@H]3[C@@H]1CCC2=O\tCHEMBL3623221\n",
      "c1ccc(-c2nnc3n2-c2ccc(-c4cccnc4)cc2CC3)cc1\tCHEMBL3622434\n",
      "COC(=O)c1cncc(-c2ccc3c(c2)CCc2nnc(C)n2-3)c1\tCHEMBL3622437\n",
      "COC(C)(C)c1cncc(-c2ccc3c(c2)CCc2nnc(C)n2-3)c1\tCHEMBL2331704\n",
      "Cc1nnc2n1-c1ccc(-c3cncc([C@](C)(O)C(F)(F)F)c3)cc1CC2\tCHEMBL3622445\n",
      "CCS(=O)(=O)NCc1cncc(-c2c(C#N)c3ccc(Cl)cc3n2C)c1\tCHEMBL3747562\n",
      "C[C@@H]1C[C@@]2(C)C(=CC1=O)CC[C@H]1[C@@H]3CCC(=O)[C@@]3(C)CC[C@@H]12\tCHEMBL3752414\n",
      "CC1(C)C[C@@]2(C)C(=CC1=O)CC[C@H]1[C@@H]3CCC(=O)[C@@]3(C)CC[C@@H]12\tCHEMBL3753169\n",
      "C=C1C[C@@]2(C)C(=CC1=O)CC[C@H]1[C@@H]3CCC(=O)[C@@]3(C)CC[C@@H]12\tCHEMBL3753307\n",
      "C[C@H]1C[C@@]2(C)C(=CC1=O)CC[C@H]1[C@@H]3CCC(=O)[C@@]3(C)CC[C@@H]12\tCHEMBL3753403\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=C(O)C(=O)CC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL132530\n",
      "CC1=C[C@@]2(C)C(=CC1=O)CC[C@@H]1[C@@H]2CC[C@]2(C)C(=O)CC[C@@H]12\tCHEMBL3754183\n",
      "CC1=C[C@@]2(C)C(=CC1=O)[C@@H](Br)C[C@@H]1[C@@H]2CC[C@]2(C)C(=O)CC[C@@H]12\tCHEMBL3753352\n",
      "C[C@]12C=C(CCCc3ccccc3)C(=O)C=C1CC[C@@H]1[C@@H]2CC[C@]2(C)C(=O)CC[C@@H]12\tCHEMBL3753611\n",
      "C[C@]12CC[C@H]3[C@@H](CC(=O)C4=C[C@@H](OC=O)CC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL3754516\n",
      "CC(=O)O[C@@H]1C=C2C(=O)C[C@H]3[C@@H]4CCC(=O)[C@@]4(C)CC[C@@H]3[C@@]2(C)CC1\tCHEMBL3752351\n",
      "C[C@]12CC[C@H]3[C@@H](CC(=O)C4=CCCC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL3752280\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=C(Sc5ccccc5)C(=O)CC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL3754241\n",
      "C[C@]12CC[C@H]3[C@H]([C@@H]1CCC2=O)[C@H](Sc1ccccc1)CC1=C(Oc2ccccc2)C(=O)CC[C@@]13C\tCHEMBL3754489\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=C(Oc5ccccc5)C(=O)CC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL484584\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=C(SC(F)F)C(=O)CC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL2113524\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=C(SCF)C(=O)CC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL3752892\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=C(SCCl)C(=O)CC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL3754724\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=C(c5ccc(OS(N)(=O)=O)cc5)C(=O)CC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL3754538\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=CCCC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL1629804\n",
      "C[C@]12CC[C@H]3[C@@H](CC[C@H]4[C@@H]5O[C@@H]5CC[C@]34C)[C@@H]1CCC2=O\tCHEMBL1629802\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=C[C@@H](O)CC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL1077603\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=C(Sc5ccccc5)C(=O)CC[C@@]43C)[C@@H]1C[C@H](C#N)[C@@H]2O\tCHEMBL3623217\n",
      "C[C@]12CC[C@H]3[C@@H](C[C@H](OC(=O)CBr)C4=CC(=O)CC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL3753645\n",
      "C=C1C[C@@H]2[C@H](CC[C@]3(C)C(=O)CC[C@@H]23)[C@@]2(C)C=CC(=O)C=C12\tCHEMBL1200374\n",
      "C[C@]12C=CC(=O)C=C1[C@@H](CC[N+](=O)[O-])C[C@@H]1[C@@H]2CC[C@]2(C)C(=O)CC[C@@H]12\tCHEMBL3751946\n",
      "C[C@]12C=CC(=O)C=C1[C@@H](CC=O)C[C@@H]1[C@@H]2CC[C@]2(C)C(=O)CC[C@@H]12\tCHEMBL3753463\n",
      "COC(=O)C1=C(C)NC(C)=C(C(=O)OC)C1C[C@H]1C[C@@H]2[C@H](CC[C@]3(C)C(=O)CC[C@@H]23)[C@@]2(C)C=CC(=O)C=C12\tCHEMBL3752191\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=CC(=O)CC[C@@]43C)[C@@H]1C[C@H](n1ccnc1)C2=O\tCHEMBL3623213\n",
      "C[C@]12C=CC(=O)C=C1CC[C@@H]1[C@@H]2CC[C@]2(C)C(=O)[C@@H](n3ccnc3)C[C@@H]12\tCHEMBL3623214\n",
      "CC(=O)O[C@@H]1[C@@H](N=[N+]=[N-])C2=CC(=O)CC[C@]2(C)[C@H]2CC[C@]3(C)C(=O)CC[C@H]3[C@H]12\tCHEMBL3753842\n",
      "C[C@]12CC[C@H]3[C@@H](/C(=N/OCCCn4ccnc4)C=C4C[C@@H](O)CC[C@@]43C)[C@@H]1CC[C@@H]2O\tCHEMBL3753778\n",
      "CC(=O)O[C@H]1CC[C@@]2(C)C(=C/C(=N\\OCCCn3ccnc3)[C@H]3[C@@H]4CC[C@H](OC(C)=O)[C@@]4(C)CC[C@@H]32)C1\tCHEMBL3752381\n",
      "C=CC[C@@H]1CC2=CC(=O)C=C[C@]2(C)[C@H]2CC[C@]3(C)C(=O)CC[C@H]3[C@@H]21\tCHEMBL3623223\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=CC(=O)CC[C@@]43C(=O)C(F)(F)F)[C@@H]1CCC2=O\tCHEMBL3753182\n",
      "CSCC[C@]12CCC(=O)C=C1CC[C@@H]1C2=CC[C@]2(C)C(=O)CC[C@@H]12\tCHEMBL3752510\n",
      "C[C@]12CC=C3[C@@H](CCC4=CC(=O)CC[C@@]43CCSCF)[C@@H]1CCC2=O\tCHEMBL3753104\n",
      "C[C@]12CC[C@H]3[C@@H](Cc4noc5c4[C@]3(C)CC[C@@H]5O)[C@@H]1CC[C@@H]2O\tCHEMBL3751861\n",
      "C[C@]12CC[C@H]3[C@@H](Cc4noc5c4[C@]3(C)CCC5=O)[C@@H]1CCC2=O\tCHEMBL3752050\n",
      "C[C@]12CC[C@H]3[C@@H](Cc4noc5c4[C@]3(C)CCC5=O)[C@@H]1CC[C@@H]2O\tCHEMBL3752637\n",
      "C[C@]12CC[C@H]3[C@@H](CC[C@H]4C(=O)C(O)=C(C#N)C[C@]34C)[C@@H]1CC[C@@H]2O\tCHEMBL3623216\n",
      "C[C@]12CC[C@H]3[C@@H](CC[C@H]4C(=O)c5[nH]ncc5C[C@]34C)[C@@H]1CC[C@@H]2O\tCHEMBL3623215\n",
      "COc1ccc([C@@H]2[C@H]3C[C@H]4[C@@H]5CCC6=CC(=O)C=C[C@]6(C)[C@H]5CC[C@]4(C)C3=NN2C)cc1\tCHEMBL3752216\n",
      "C[C@]12C=CC(=O)C=C1CC[C@@H]1[C@@H]2CC[C@]2(C)C3=NN(c4ccccc4)[C@H](c4ccccc4)[C@H]3C[C@@H]12\tCHEMBL3754253\n",
      "COc1ccc([C@@H]2[C@H]3C[C@H]4[C@@H]5CCC6=CC(=O)C=C[C@]6(C)[C@H]5CC[C@]4(C)C3=NN2c2ccccc2)cc1\tCHEMBL3754378\n",
      "COc1cc(/C=C2\\C[C@H]3[C@@H]4CCC5=CC(=O)CC[C@]5(C)[C@H]4CC[C@]3(C)C2=O)ccc1OCCCn1ccnc1\tCHEMBL3752689\n",
      "COc1cc(/C=C2\\C[C@H]3[C@@H]4CC=C5C[C@@H](O)CC[C@]5(C)[C@H]4CC[C@]3(C)[C@H]2O)ccc1OCCCn1ccnc1\tCHEMBL3623212\n",
      "C[C@]12CC[C@H]3[C@@H](CC4=Nc5ccccc5SC5=C4[C@]3(C)CCC5=O)[C@@H]1CCC2=O\tCHEMBL3754157\n",
      "C[C@]12CC[C@H]3[C@@H](Cc4nn(Cc5ccccc5)c5c4[C@]3(C)CCC5=O)[C@@H]1CC[C@@H]2O\tCHEMBL3751913\n",
      "C[C@]12CC[C@H]3[C@@H](Cc4n[nH]c5c4[C@]3(C)CCC5=O)[C@@H]1CC[C@@H]2O\tCHEMBL3751984\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=CC(=O)CC[C@@]43C)[C@@H]1C/C(=C\\c1ccncc1)C2=O\tCHEMBL2003274\n",
      "C[C@]12C=CC(=O)C=C1CC[C@@H]1[C@@H]2CC[C@]2(C)C(=O)/C(=C/c3ccc4c(c3)OCO4)C[C@@H]12\tCHEMBL3752285\n",
      "CN1N=C2[C@H](C[C@H]3[C@@H]4CCC5=CC(=O)C=C[C@]5(C)[C@H]4CC[C@]23C)[C@H]1c1ccccc1\tCHEMBL3752166\n",
      "O=c1c(Cc2ccncc2)coc2ccccc12\tCHEMBL3794430\n",
      "O=c1c(Cc2ccncc2)coc2cc(F)ccc12\tCHEMBL3793091\n",
      "O=c1cc(Cc2ccncc2)oc2cc(F)ccc12\tCHEMBL3794417\n",
      "COc1ccc2c(=O)c(Cc3c[nH]cn3)coc2c1\tCHEMBL3793530\n",
      "O=c1c(Cc2c[nH]cn2)coc2cc(F)ccc12\tCHEMBL3793576\n",
      "C[C@]12CCC(=O)C=C1CC[C@@H]1C2=CC[C@]2(C)C(=O)CC[C@@H]12\tCHEMBL3799363\n",
      "C[C@]12CC[C@H]3[C@@H](CC[C@]45O[C@H]4CCC[C@]35C)[C@@H]1CCC2=O\tCHEMBL3800417\n",
      "C[C@]12CC[C@H]3[C@@H](CC[C@H]4[C@@H]5C[C@@H]5CC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL3798461\n",
      "C=CC[C@@H]1CC2=CC(=O)C=C[C@]2(C)[C@H]2CC[C@]3(C)C(=O)CC[C@H]3[C@@H]21\tCHEMBL3623223\n",
      "C[C@H](O)C/C=C/CCCCC1Cc2cc(O)cc(O)c2C(=O)O1\tCHEMBL3799189\n",
      "COC(=O)c1ccccc1S(=O)(=O)N1CCCC(Cn2ccnc2)C1\tCHEMBL3805021\n",
      "Cc1cc(S(=O)(=O)N2CCCC(Cn3ccnc3)C2)ccc1F\tCHEMBL3805310\n",
      "COc1ccc(S(=O)(=O)N2CCCC(Cn3ccnc3)C2)cc1F\tCHEMBL3805173\n",
      "O=[N+]([O-])c1cccc(Cl)c1Oc1cccc(S(=O)(=O)N2CCCC(Cn3ccnc3)C2)c1\tCHEMBL3806301\n",
      "O=S(=O)(c1ccc(F)c(Cl)c1)N1CCCC(Cn2ccnc2)C1\tCHEMBL3805481\n",
      "O=S(=O)(c1ccc2c(c1)OCCO2)N1CCCC(Cn2ccnc2)C1\tCHEMBL3805211\n",
      "O=S(=O)(c1cccc2nsnc12)N1CCCC(Cn2ccnc2)C1\tCHEMBL3806151\n",
      "O=S(=O)(c1ccc2nsnc2c1)N1CCCC(Cn2ccnc2)C1\tCHEMBL3805851\n",
      "O=C(O)c1cc(S(=O)(=O)N2CCCC(Cn3ccnc3)C2)ccc1O\tCHEMBL3804849\n",
      "COc1ccc(S(=O)(=O)N2CCCC(Cn3ccnc3)C2)c(OC)c1\tCHEMBL3805678\n",
      "N#Cc1ccc(S(=O)(=O)N2CCCC(Cn3ccnc3)C2)c(Cl)c1\tCHEMBL3806282\n",
      "O=S(=O)(c1ccc(F)c(F)c1)N1CCCC(Cn2ccnc2)C1\tCHEMBL3806159\n",
      "N#Cc1ccc(S(=O)(=O)N2CCCC(Cn3ccnc3)C2)cc1\tCHEMBL3806046\n",
      "O=S(=O)(c1ccc(F)c(F)c1F)N1CCCC(Cn2ccnc2)C1\tCHEMBL3805839\n",
      "COc1ccc(S(=O)(=O)N2CCCC(Cn3ccnc3)C2)cc1Br\tCHEMBL3805814\n",
      "COc1ccc(S(=O)(=O)N2CCCC(Cn3ccnc3)C2)cc1OC\tCHEMBL3805149\n",
      "O=S(=O)(c1ccc(F)cc1)N1CCCC(Cn2ccnc2)C1\tCHEMBL3805048\n",
      "Cc1cc(F)ccc1S(=O)(=O)N1CCCC(Cn2ccnc2)C1\tCHEMBL3806185\n",
      "COc1cc([N+](=O)[O-])ccc1S(=O)(=O)N1CCCC(Cn2ccnc2)C1\tCHEMBL3804993\n",
      "CS(=O)(=O)c1ccc(S(=O)(=O)N2CCCC(Cn3ccnc3)C2)cc1\tCHEMBL3805657\n",
      "O=[N+]([O-])c1ccc(S(=O)(=O)N2CCCC(Cn3ccnc3)C2)cc1C(F)(F)F\tCHEMBL3805008\n",
      "COc1c(C)cc(S(=O)(=O)N2CCCC(Cn3ccnc3)C2)c(C)c1C\tCHEMBL3805388\n",
      "COc1c(C)cc(S(=O)(=O)N2CCCC(Cn3ccnc3)C2)cc1C\tCHEMBL3806075\n",
      "COc1ccc(S(=O)(=O)N2CCCC(Cn3ccnc3)C2)c(C)c1C\tCHEMBL3805820\n",
      "O=S(=O)(c1ccc(OC2CCCC2)c(F)c1)N1CCCC(Cn2ccnc2)C1\tCHEMBL3805539\n",
      "O=S(=O)(c1ccc(OC2CCCC2)cc1)N1CCCC(Cn2ccnc2)C1\tCHEMBL3805733\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL1444\n",
      "COc1ccc(S(=O)(=O)N2CCCC(Cn3ccnc3)C2)cc1C(C)(C)C\tCHEMBL1784801\n",
      "Cn1nnc2ccc(C(c3ccc(Cl)cc3)n3cncn3)cc21\tCHEMBL100749\n",
      "CC/C(=C(\\c1ccc(O)cc1)c1ccc(OCCNC)cc1)c1ccccc1\tCHEMBL1093458\n",
      "CC/C(=C(\\c1ccc(O)cc1)c1ccc(OCCN)cc1)c1ccccc1\tCHEMBL2386284\n",
      "CC/C(=C(\\c1ccc(O)cc1)c1ccc(OCCN(C)C)cc1)c1ccccc1\tCHEMBL489\n",
      "N#Cc1ccc(C(Cc2ccc(OS(N)(=O)=O)c(Br)c2)n2cncn2)cc1\tCHEMBL3818343\n",
      "N#Cc1ccc(N(Cc2ccc(OS(N)(=O)=O)cc2F)n2ccnc2)cc1\tCHEMBL3623232\n",
      "Fc1ccc(-c2cc(-n3cccc3)c3ccccc3n2)cc1\tCHEMBL3818279\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=C(Sc5ccccc5)C(=O)CC[C@@]43C)[C@@H]1C[C@H](C#N)[C@@H]2O\tCHEMBL3623217\n",
      "C[C@]12CC[C@H]3[C@@H](CC[C@H]4C(=O)C(O)=C(C#N)C[C@]34C)[C@@H]1CC[C@@H]2O\tCHEMBL3623216\n",
      "C[C@]12CC[C@H]3[C@@H](CC[C@H]4C(=O)c5[nH]ccc5C[C@]34C)[C@@H]1CC[C@@H]2O\tCHEMBL3818442\n",
      "C[C@]12C=CC(=O)C=C1CC[C@@H]1[C@@H]2CC[C@]2(C)C(=O)C(n3ccnc3)C[C@@H]12\tCHEMBL3818725\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=CC(=O)CC[C@@]43C)[C@@H]1CC(n1ccnc1)C2=O\tCHEMBL3819184\n",
      "C=CC[C@@H]1CC2=CC(=O)C=C[C@]2(C)[C@H]2CC[C@]3(C)C(=O)CC[C@H]3[C@@H]21\tCHEMBL3623223\n",
      "C=CC[C@@H]1CC2=CC(=O)C=C[C@]2(C)[C@H]2CC[C@]3(C)[C@@H](O)CC[C@H]3[C@@H]21\tCHEMBL3819549\n",
      "C=CC[C@@H]1CC2=CCCC[C@]2(C)[C@H]2CC[C@]3(C)C(=O)CC[C@H]3[C@@H]21\tCHEMBL3818338\n",
      "C=CC[C@@H]1CC2=CC(=O)CC[C@]2(C)[C@H]2CC[C@]3(C)C(=O)CC[C@H]3[C@@H]21\tCHEMBL3818728\n",
      "CC(C)(C#N)c1cc(Cn2cncn2)cc(C(C)(C)C#N)c1\tCHEMBL1399\n",
      "N#Cc1ccc(C2CCCc3cncn32)cc1\tCHEMBL9298\n",
      "C[C@]12CC[C@H]3[C@@H](CCC4=C(O)C(=O)CC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL132530\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "COc1ccc2c(Cc3ccccc3)c(-c3ccc(Cl)cc3)c(=O)oc2c1\tCHEMBL3818720\n",
      "O=C1C[C@@H](c2ccc(O)cc2)Oc2cc(O)cc(O)c21\tCHEMBL9352\n",
      "O=c1cc(Cn2ccnc2)c2ccc(Oc3ccccc3)cc2o1\tCHEMBL437731\n",
      "COc1cc(C2CC(=O)Oc3cc4c(cc32)OCO4)cc(OC)c1O\tCHEMBL3623228\n",
      "COc1cc(C2CC(=O)Oc3cc4c(cc32)OCO4)cc(OC)c1OC(C)=O\tCHEMBL3827037\n",
      "O=C1C[C@@H](c2ccc(O)cc2)Oc2cc(O)cc(O)c21\tCHEMBL9352\n",
      "CC/C(=C(/c1ccc(O)cc1)c1ccc(OCC(N)=O)cc1)c1ccccc1\tCHEMBL3942918\n",
      "CCC(=C(c1ccc(N)cc1)c1ccc(NCC(N)=O)cc1)c1ccc([N+](=O)[O-])cc1\tCHEMBL3961452\n",
      "CC(=C(c1ccc(N)cc1)c1ccc(NCC(N)=O)cc1)c1ccc([N+](=O)[O-])cc1\tCHEMBL3961033\n",
      "CC(=C(c1ccc(O)cc1)c1ccc(OCC(N)=O)cc1)c1ccc([N+](=O)[O-])cc1\tCHEMBL3925065\n",
      "CCC(=C(c1ccc(N)cc1)c1ccc(N)cc1)c1ccc(N)cc1\tCHEMBL3944017\n",
      "CC(=C(c1ccc(N)cc1)c1ccc(N)cc1)c1ccc(N)cc1\tCHEMBL3916057\n",
      "CCC(=C(c1ccc(O)cc1)c1ccc(O)cc1)c1ccc(N)cc1\tCHEMBL3948254\n",
      "CC(=C(c1ccc(O)cc1)c1ccc(O)cc1)c1ccc(N)cc1\tCHEMBL3977491\n",
      "CCC(=C(c1ccc(O)cc1)c1ccc(OCCN)cc1)c1ccccc1\tCHEMBL3350384\n",
      "CC/C(=C(/c1ccc(O)cc1)c1ccc(OCCN)cc1)c1ccccc1\tCHEMBL2386285\n",
      "CC/C(=C(\\c1ccc(O)cc1)c1ccc(OCCN)cc1)c1ccccc1\tCHEMBL2386284\n",
      "CCC(=C(c1ccc(O)cc1)c1ccc(O)cc1)c1ccccc1\tCHEMBL149791\n",
      "CCC(=C(c1ccc(N)cc1)c1ccc(N)cc1)c1ccc([N+](=O)[O-])cc1\tCHEMBL3905734\n",
      "CC(=C(c1ccc(N)cc1)c1ccc(N)cc1)c1ccc([N+](=O)[O-])cc1\tCHEMBL3984531\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL1444\n",
      "CC(=O)N1CCN(c2ccc(OCC3COC(Cn4ccnc4)(c4ccc(Cl)cc4Cl)O3)cc2)CC1\tCHEMBL157101\n",
      "CS(=O)(=O)N1CCN(c2ccc(OC[C@@H]3CO[C@@](Cn4ccnc4)(c4ccc(Cl)cc4Cl)O3)cc2)CC1\tCHEMBL3983734\n",
      "CCS(=O)(=O)N1CCN(c2ccc(OC[C@@H]3CO[C@@](Cn4ccnc4)(c4ccc(Cl)cc4Cl)O3)cc2)CC1\tCHEMBL3913908\n",
      "CC(C)S(=O)(=O)N1CCN(c2ccc(OC[C@@H]3CO[C@@](Cn4ccnc4)(c4ccc(Cl)cc4Cl)O3)cc2)CC1\tCHEMBL3894912\n",
      "O=S(=O)(C1CC1)N1CCN(c2ccc(OC[C@@H]3CO[C@@](Cn4ccnc4)(c4ccc(Cl)cc4Cl)O3)cc2)CC1\tCHEMBL3922888\n",
      "COCCS(=O)(=O)N1CCN(c2ccc(OC[C@@H]3CO[C@@](Cn4ccnc4)(c4ccc(Cl)cc4Cl)O3)cc2)CC1\tCHEMBL3964421\n",
      "O=S(=O)(C1CCOCC1)N1CCN(c2ccc(OC[C@@H]3CO[C@@](Cn4ccnc4)(c4ccc(Cl)cc4Cl)O3)cc2)CC1\tCHEMBL3931836\n",
      "O=S(=O)(N1CCN(c2ccc(OC[C@@H]3CO[C@@](Cn4ccnc4)(c4ccc(Cl)cc4Cl)O3)cc2)CC1)C(F)(F)F\tCHEMBL3938615\n",
      "O=S(=O)(C(F)F)N1CCN(c2ccc(OC[C@@H]3CO[C@@](Cn4ccnc4)(c4ccc(Cl)cc4Cl)O3)cc2)CC1\tCHEMBL3968178\n",
      "N#CCS(=O)(=O)N1CCN(c2ccc(OC[C@@H]3CO[C@@](Cn4ccnc4)(c4ccc(Cl)cc4Cl)O3)cc2)CC1\tCHEMBL3952032\n",
      "CS(=O)(=O)CS(=O)(=O)N1CCN(c2ccc(OC[C@@H]3CO[C@@](Cn4ccnc4)(c4ccc(Cl)cc4Cl)O3)cc2)CC1\tCHEMBL3980889\n",
      "O=S(=O)(c1cccs1)N1CCN(c2ccc(OC[C@@H]3CO[C@@](Cn4ccnc4)(c4ccc(Cl)cc4Cl)O3)cc2)CC1\tCHEMBL3960724\n",
      "Cn1ccnc1S(=O)(=O)N1CCN(c2ccc(OC[C@@H]3CO[C@@](Cn4ccnc4)(c4ccc(Cl)cc4Cl)O3)cc2)CC1\tCHEMBL3891718\n",
      "C[C@](O)(c1cncc(-c2nc3ccc(F)cn3c2C2CC2)c1)C(F)(F)F\tCHEMBL3923127\n",
      "Fc1cncc(-c2nc3ccc(F)cn3c2C2CC2)c1\tCHEMBL3893614\n",
      "Fc1ccc2nc(-c3cncc4ccccc34)c(C3CC3)n2c1\tCHEMBL3948337\n",
      "Fc1cncc(-c2nc3cc(F)c(Cl)cn3c2C2CC2)c1\tCHEMBL3951973\n",
      "Cc1c(F)cncc1-c1nc2ccc(F)cn2c1C1CC1\tCHEMBL3924257\n",
      "CCc1c(F)cncc1-c1nc2ccc(F)cn2c1C1CC1\tCHEMBL3919759\n",
      "O=C(/C=C\\Nc1ccc(S(=O)(=O)Nc2ccnn2-c2ccccc2)cc1)c1cc2ccccc2oc1=O\tCHEMBL3959541\n",
      "O=C(/C=C\\Nc1ccc(S(=O)(=O)Nc2ccccn2)cc1)c1cc2ccccc2oc1=O\tCHEMBL3941042\n",
      "Cc1ccnc(NS(=O)(=O)c2ccc(N/C=C\\C(=O)c3cc4ccccc4oc3=O)cc2)n1\tCHEMBL3914106\n",
      "COc1cnc(NS(=O)(=O)c2ccc(N/C=C\\C(=O)c3cc4ccccc4oc3=O)cc2)nc1\tCHEMBL3924170\n",
      "COc1ncnc(NS(=O)(=O)c2ccc(N/C=C\\C(=O)c3cc4ccccc4oc3=O)cc2)c1OC\tCHEMBL3926174\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL1444\n",
      "c1cncc(-c2ccc3ccccc3c2)c1\tCHEMBL365735\n",
      "COc1ccc2cc(-c3cccnc3)ccc2c1\tCHEMBL362968\n",
      "CCOc1ccc2cc(-c3cccnc3)ccc2c1\tCHEMBL193343\n",
      "COc1ccc2cc(-c3cccnc3)ccc2c1Cl\tCHEMBL197576\n",
      "COc1ccc2cc(-c3cccnc3)ccc2c1Br\tCHEMBL196328\n",
      "COc1ccc2c(Cl)c(-c3cccnc3)ccc2c1Cl\tCHEMBL381014\n",
      "COC(=O)c1ccc2cc(-c3cccnc3)ccc2c1\tCHEMBL438802\n",
      "c1ccc2cc(-n3ccnc3)ccc2c1\tCHEMBL195996\n",
      "COc1cc2ccccc2cc1-n1ccnc1\tCHEMBL194472\n",
      "N#Cc1ccc(C2CCCc3cncn32)cc1\tCHEMBL9298\n",
      "C1=C(c2cccnc2)CCc2ccccc21\tCHEMBL205881\n",
      "COc1ccc2c(c1)CCC(c1cccnc1)=C2\tCHEMBL206351\n",
      "CC1=C(c2cccnc2)CCc2ccccc21\tCHEMBL206283\n",
      "CCC1=C(c2cccnc2)CCc2ccccc21\tCHEMBL205503\n",
      "CC1Cc2ccccc2C=C1c1cccnc1\tCHEMBL206550\n",
      "CC1CC(c2cccnc2)=Cc2ccccc21\tCHEMBL206070\n",
      "N#Cc1ccc(C2CCCc3cncn32)cc1\tCHEMBL9298\n",
      "N#Cc1ccc(C2CCCc3cncn32)cc1\tCHEMBL9298\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "CCN(CC)c1ccc2cc(-c3cc4ccc(N(CC)CC)cc4oc3=O)c(=O)oc2c1\tCHEMBL4070331\n",
      "COc1ccc2cc(-c3cc4ccc(OC)cc4oc3=O)c(=O)oc2c1\tCHEMBL4091260\n",
      "O=c1ccc2ccc(-c3ccncc3)cc2o1\tCHEMBL4080965\n",
      "O=c1ccc2ccc(-c3cccnc3)cc2o1\tCHEMBL4098990\n",
      "O=c1ccc2ccc(-c3ccccn3)cc2o1\tCHEMBL4099909\n",
      "COc1ccc(-c2ccc3ccc(=O)oc3c2)cc1\tCHEMBL1650711\n",
      "O=c1ccc2ccc(-c3ccccc3)cc2o1\tCHEMBL1650709\n",
      "COc1ccc2ccc(=O)oc2c1\tCHEMBL49732\n",
      "CCN(CC)c1ccc2cc(-c3coc(-c4ccccc4)n3)c(=O)oc2c1\tCHEMBL4100188\n",
      "CCOC(=O)c1oc(-c2cc3ccc(N(CC)CC)cc3oc2=O)nc1C\tCHEMBL4074186\n",
      "CCN(CC)c1ccc2cc(-c3ncco3)c(=O)oc2c1\tCHEMBL4105495\n",
      "CCN(CC)c1ccc2cc(-c3cnco3)c(=O)oc2c1\tCHEMBL4087613\n",
      "CCN(CC)c1ccc2cc(-c3nc(-c4ccccc4)cs3)c(=O)oc2c1\tCHEMBL4063099\n",
      "CCN(CC)c1ccc2cc(-c3nc(CC(=O)OC)cs3)c(=O)oc2c1\tCHEMBL4095313\n",
      "CCOC(=O)c1csc(-c2cc3ccc(N(CC)CC)cc3oc2=O)n1\tCHEMBL4078790\n",
      "CCN(CC)c1ccc2cc(-c3nc(C)cs3)c(=O)oc2c1\tCHEMBL4103066\n",
      "CCN(CC)c1ccc2ccc(=O)oc2c1\tCHEMBL1430816\n",
      "CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL488\n",
      "C=C1C[C@@H]2[C@H](CC[C@]3(C)C(=O)CC[C@@H]23)[C@@]2(C)C=CC(=O)C=C12\tCHEMBL1200374\n",
      "Cc1nn(-c2cncc([C@](C)(O)C(F)(F)F)c2)c2ccccc12\tCHEMBL4098297\n",
      "Cc1nn(-c2cncc([C@@](C)(O)C(F)(F)F)c2)c2ccccc12\tCHEMBL4080256\n",
      "C[C@](O)(c1cncc(-n2nc(C(F)(F)F)c3ccccc32)c1)C(F)(F)F\tCHEMBL4071689\n",
      "Cc1nn(-c2cncc(C(C)(C)O)c2)c2cccc(F)c12\tCHEMBL4080420\n",
      "CC(C)(O)c1cncc(-n2ncc3c(C(F)(F)F)cccc32)c1\tCHEMBL4067000\n",
      "O=C(/C=C\\Nc1ccc(S(=O)(=O)Nc2nccs2)cc1)c1ccc2c(c1)Nc1ccccc1S2\tCHEMBL4060208\n",
      "N=C(N)NS(=O)(=O)c1ccc(N/C=C\\C(=O)c2ccc3c(c2)Nc2ccccc2S3)cc1\tCHEMBL4077259\n",
      "O=C(/C=C\\Nc1ccc(S(=O)(=O)Nc2ccccn2)cc1)c1ccc2c(c1)Nc1ccccc1S2\tCHEMBL4098604\n",
      "O=C(/C=C\\Nc1ccc(S(=O)(=O)Nc2cnc3ccccc3n2)cc1)c1ccc2c(c1)Nc1ccccc1S2\tCHEMBL4068601\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL1444\n",
      "CC[C@@H]([C@H](C)O)n1ncn(-c2ccc(N3CCN(c4ccc(OC[C@@H]5CO[C@@](Cn6cncn6)(c6ccc(F)cc6F)C5)cc4)CC3)cc2)c1=O\tCHEMBL1397\n",
      "OC(Cn1cncn1)(Cn1cncn1)c1ccc(F)cc1F\tCHEMBL106\n",
      "N#Cc1ccc(COc2ccc(C#Cc3ccc(C(F)(F)C(O)(Cn4cnnn4)c4ccc(F)cc4F)nc3)cc2)cc1\tCHEMBL4077246\n",
      "CC(=O)N1CCN(c2ccc(OC[C@H]3CO[C@](Cn4ccnc4)(c4ccc(Cl)cc4Cl)O3)cc2)CC1\tCHEMBL75\n",
      "CC(=O)N1CCN(c2ccc(/C=C/c3cc(F)cc(-c4ccncc4)c3)cc2)CC1\tCHEMBL4129721\n",
      "CCS(=O)(=O)N1CCN(c2ccc(/C=C/c3cc(F)cc(-c4ccncc4)c3)cc2)CC1\tCHEMBL4126284\n",
      "O=S(=O)(N1CCN(c2ccc(/C=C/c3cc(F)cc(-c4ccncc4)c3)cc2)CC1)C(F)(F)F\tCHEMBL4128368\n",
      "Cc1ccc(S(=O)(=O)NCCc2c(C(c3ccccc3)c3[nH]c4ccccc4c3CCNS(=O)(=O)c3ccc(C)cc3)[nH]c3ccccc23)cc1\tCHEMBL4170393\n",
      "O=[N+]([O-])c1ccc(S(=O)(=O)NCCc2c(C(c3ccccc3)c3[nH]c4ccccc4c3CCNS(=O)(=O)c3ccc([N+](=O)[O-])cc3)[nH]c3ccccc23)cc1\tCHEMBL4172955\n",
      "COc1ccc(S(=O)(=O)NCCc2c(C(c3ccccc3)c3[nH]c4ccccc4c3CCNS(=O)(=O)c3ccc(OC)cc3)[nH]c3ccccc23)cc1\tCHEMBL4165064\n",
      "O=[N+]([O-])c1ccc(C(c2[nH]c3ccccc3c2CCNS(=O)(=O)c2ccc(Br)cc2)c2[nH]c3ccccc3c2CCNS(=O)(=O)c2ccc(Br)cc2)cc1\tCHEMBL4174637\n",
      "O=[N+]([O-])c1ccc(C(c2[nH]c3ccccc3c2CCNS(=O)(=O)c2ccc(F)cc2)c2[nH]c3ccccc3c2CCNS(=O)(=O)c2ccc(F)cc2)cc1\tCHEMBL4163983\n",
      "O=[N+]([O-])c1ccc(C(c2[nH]c3ccccc3c2CCNS(=O)(=O)c2ccc(C(F)(F)F)cc2)c2[nH]c3ccccc3c2CCNS(=O)(=O)c2ccc(C(F)(F)F)cc2)cc1\tCHEMBL4159524\n",
      "N#Cc1ccc(C(c2[nH]c3ccccc3c2CCNS(=O)(=O)c2ccc(Cl)cc2)c2[nH]c3ccccc3c2CCNS(=O)(=O)c2ccc(Cl)cc2)cc1\tCHEMBL4167430\n",
      "N#Cc1ccc(C(c2[nH]c3ccccc3c2CCNS(=O)(=O)c2ccc(F)cc2)c2[nH]c3ccccc3c2CCNS(=O)(=O)c2ccc(F)cc2)cc1\tCHEMBL4167841\n",
      "N#Cc1ccc(C(c2[nH]c3ccccc3c2CCNS(=O)(=O)c2ccc(C(F)(F)F)cc2)c2[nH]c3ccccc3c2CCNS(=O)(=O)c2ccc(C(F)(F)F)cc2)cc1\tCHEMBL4175698\n",
      "O=S(=O)(NCCc1c(C(c2ccc(Br)cc2)c2[nH]c3ccccc3c2CCNS(=O)(=O)c2ccc(Cl)cc2)[nH]c2ccccc12)c1ccc(Cl)cc1\tCHEMBL4171198\n",
      "O=S(=O)(NCCc1c(C(c2ccc(Br)cc2)c2[nH]c3ccccc3c2CCNS(=O)(=O)c2ccc(C(F)(F)F)cc2)[nH]c2ccccc12)c1ccc(C(F)(F)F)cc1\tCHEMBL4160591\n",
      "O=S(=O)(NCCc1c(C(c2ccc(F)cc2)c2[nH]c3ccccc3c2CCNS(=O)(=O)c2ccc(Br)cc2)[nH]c2ccccc12)c1ccc(Br)cc1\tCHEMBL4169756\n",
      "O=S(=O)(NCCc1c(C(c2ccc(F)cc2)c2[nH]c3ccccc3c2CCNS(=O)(=O)c2ccc(Cl)cc2)[nH]c2ccccc12)c1ccc(Cl)cc1\tCHEMBL4159101\n",
      "O=S(=O)(NCCc1c(C(c2ccc(F)cc2)c2[nH]c3ccccc3c2CCNS(=O)(=O)c2ccc(C(F)(F)F)cc2)[nH]c2ccccc12)c1ccc(C(F)(F)F)cc1\tCHEMBL4167098\n",
      "O=S(=O)(NCCc1c(C(c2ccc(C(F)(F)F)cc2)c2[nH]c3ccccc3c2CCNS(=O)(=O)c2ccc(Br)cc2)[nH]c2ccccc12)c1ccc(Br)cc1\tCHEMBL4159184\n",
      "O=S(=O)(NCCc1c(C(c2ccc(C(F)(F)F)cc2)c2[nH]c3ccccc3c2CCNS(=O)(=O)c2ccc(Cl)cc2)[nH]c2ccccc12)c1ccc(Cl)cc1\tCHEMBL4163649\n",
      "O=S(=O)(NCCc1c(C(c2ccc(C(F)(F)F)cc2)c2[nH]c3ccccc3c2CCNS(=O)(=O)c2ccc(C(F)(F)F)cc2)[nH]c2ccccc12)c1ccc(C(F)(F)F)cc1\tCHEMBL4174326\n",
      "COc1ccc(S(=O)(=O)NCCc2c(C(c3ccc(O)cc3)c3[nH]c4ccccc4c3CCNS(=O)(=O)c3ccc(OC)cc3)[nH]c3ccccc23)cc1\tCHEMBL4173941\n",
      "O=S(=O)(NCCc1c(C(c2ccc(O)cc2)c2[nH]c3ccccc3c2CCNS(=O)(=O)c2ccc(Cl)cc2)[nH]c2ccccc12)c1ccc(Cl)cc1\tCHEMBL4166016\n",
      "O=S(=O)(NCCc1c(C(c2ccc(O)cc2)c2[nH]c3ccccc3c2CCNS(=O)(=O)c2ccc(C(F)(F)F)cc2)[nH]c2ccccc12)c1ccc(C(F)(F)F)cc1\tCHEMBL4170504\n",
      "O=[N+]([O-])c1cccc(S(=O)(=O)NCCc2c(C(c3ccc(O)cc3)c3[nH]c4ccccc4c3CCNS(=O)(=O)c3cccc([N+](=O)[O-])c3)[nH]c3ccccc23)c1\tCHEMBL4162572\n",
      "Cc1ccc(S(=O)(=O)NCCc2c(C(c3ccccc3O)c3[nH]c4ccccc4c3CCNS(=O)(=O)c3ccc(C)cc3)[nH]c3ccccc23)cc1\tCHEMBL4162163\n",
      "O=[N+]([O-])c1ccc(S(=O)(=O)NCCc2c(C(c3ccccc3O)c3[nH]c4ccccc4c3CCNS(=O)(=O)c3ccc([N+](=O)[O-])cc3)[nH]c3ccccc23)cc1\tCHEMBL4172837\n",
      "COc1ccc(S(=O)(=O)NCCc2c(C(c3ccccc3O)c3[nH]c4ccccc4c3CCNS(=O)(=O)c3ccc(OC)cc3)[nH]c3ccccc23)cc1\tCHEMBL4161859\n",
      "O=S(=O)(NCCc1c(C(c2ccccc2O)c2[nH]c3ccccc3c2CCNS(=O)(=O)c2ccc(Cl)cc2)[nH]c2ccccc12)c1ccc(Cl)cc1\tCHEMBL4169784\n",
      "O=[N+]([O-])c1cccc(S(=O)(=O)NCCc2c(C(c3ccccc3O)c3[nH]c4ccccc4c3CCNS(=O)(=O)c3cccc([N+](=O)[O-])c3)[nH]c3ccccc23)c1\tCHEMBL4176982\n",
      "O=[N+]([O-])c1ccccc1S(=O)(=O)NCCc1c(C(c2ccccc2O)c2[nH]c3ccccc3c2CCNS(=O)(=O)c2ccccc2[N+](=O)[O-])[nH]c2ccccc12\tCHEMBL4166364\n",
      "COc1ccc(S(=O)(=O)NCCc2c(C(c3cc(OC)c(O)c(OC)c3)c3[nH]c4ccccc4c3CCNS(=O)(=O)c3ccc(OC)cc3)[nH]c3ccccc23)cc1\tCHEMBL4173619\n",
      "O=S(=O)(NCCc1c(C(c2ccc3c(c2)Cc2ccccc2-3)c2[nH]c3ccccc3c2CCNS(=O)(=O)c2ccc(Cl)cc2)[nH]c2ccccc12)c1ccc(Cl)cc1\tCHEMBL4162907\n",
      "CCn1c2ccccc2c2ccc(C(c3[nH]c4ccccc4c3CCNS(=O)(=O)c3ccc(Cl)cc3)c3[nH]c4ccccc4c3CCNS(=O)(=O)c3ccc(Cl)cc3)cc21\tCHEMBL4170175\n",
      "COc1ccc(S(=O)(=O)NCCc2c(C3(c4[nH]c5ccccc5c4CCNS(=O)(=O)c4ccc(OC)cc4)C(=O)Nc4ccccc43)[nH]c3ccccc23)cc1\tCHEMBL4159550\n",
      "O=C1Nc2ccccc2C1(c1[nH]c2ccccc2c1CCNS(=O)(=O)c1ccc(Cl)cc1)c1[nH]c2ccccc2c1CCNS(=O)(=O)c1ccc(Cl)cc1\tCHEMBL4166769\n",
      "O=C1Nc2ccccc2C1(c1[nH]c2ccccc2c1CCNS(=O)(=O)c1cccc([N+](=O)[O-])c1)c1[nH]c2ccccc2c1CCNS(=O)(=O)c1cccc([N+](=O)[O-])c1\tCHEMBL4174670\n",
      "O=C1Nc2ccccc2C1(c1[nH]c2ccccc2c1CCNS(=O)(=O)c1ccccc1[N+](=O)[O-])c1[nH]c2ccccc2c1CCNS(=O)(=O)c1ccccc1[N+](=O)[O-]\tCHEMBL4168266\n",
      "CC(=O)N1CCN(c2ccc(OC[C@H]3CO[C@](Cn4ccnc4)(c4ccc(Cl)cc4Cl)O3)cc2)CC1\tCHEMBL75\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL1444\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL1444\n",
      "Fc1ccc(-c2[nH]c3cc(F)ccc3c2Cn2ccnc2)cc1\tCHEMBL4175438\n",
      "Fc1ccc2c(Cn3ccnc3)c(-c3ccc(Cl)cc3)[nH]c2c1\tCHEMBL4167569\n",
      "Fc1ccc(-c2[nH]c3cc(Cl)ccc3c2Cn2ccnc2)cc1\tCHEMBL4171996\n",
      "Clc1ccc(-c2[nH]c3cc(Cl)ccc3c2Cn2ccnc2)cc1\tCHEMBL4164126\n",
      "COc1ccc(-c2[nH]c3cc(F)ccc3c2Cn2ccnc2)cc1\tCHEMBL4163704\n",
      "COc1ccc(-c2[nH]c3cc(Cl)ccc3c2Cn2ccnc2)cc1\tCHEMBL4174373\n",
      "COc1ccc2c(Cn3ccnc3)c(-c3ccc(F)cc3)[nH]c2c1\tCHEMBL4160335\n",
      "COc1ccc2c(Cn3ccnc3)c(-c3ccc(Cl)cc3)[nH]c2c1\tCHEMBL4170941\n",
      "Fc1ccc(-c2[nH]c3cc4c(cc3c2Cn2ccnc2)OCO4)cc1\tCHEMBL4161789\n",
      "Clc1ccc(-c2[nH]c3cc4c(cc3c2Cn2ccnc2)OCO4)cc1\tCHEMBL4172433\n",
      "Fc1ccc(-c2[nH]c3cc(F)ccc3c2Cn2cncn2)cc1\tCHEMBL4176600\n",
      "Fc1ccc2c(Cn3cncn3)c(-c3ccc(Cl)cc3)[nH]c2c1\tCHEMBL4165988\n",
      "Fc1ccc(-c2[nH]c3cc(Cl)ccc3c2Cn2cncn2)cc1\tCHEMBL4161492\n",
      "Clc1ccc(-c2[nH]c3cc(Cl)ccc3c2Cn2cncn2)cc1\tCHEMBL4169419\n",
      "COc1ccc(-c2[nH]c3cc(F)ccc3c2Cn2cncn2)cc1\tCHEMBL4169808\n",
      "COc1ccc(-c2[nH]c3cc(Cl)ccc3c2Cn2cncn2)cc1\tCHEMBL4159159\n",
      "COc1ccc2c(Cn3cncn3)c(-c3ccc(F)cc3)[nH]c2c1\tCHEMBL4173231\n",
      "COc1ccc2c(Cn3cncn3)c(-c3ccc(Cl)cc3)[nH]c2c1\tCHEMBL4162549\n",
      "Fc1ccc(-c2[nH]c3cc4c(cc3c2Cn2cncn2)OCO4)cc1\tCHEMBL4162931\n",
      "Clc1ccc(-c2[nH]c3cc4c(cc3c2Cn2cncn2)OCO4)cc1\tCHEMBL4170868\n",
      "O=C(/C=C/c1ccc(O)cc1)c1ccc(O)cc1O\tCHEMBL129795\n",
      "C#Cc1c(-c2ccccc2)[nH]c2ccccc12\tCHEMBL4210489\n",
      "c1cncc(-c2cc3ccccc3[nH]2)c1\tCHEMBL4209034\n",
      "O=[N+]([O-])c1ccc2[nH]c(-c3ccccc3)cc2c1\tCHEMBL469266\n",
      "Cn1c(-c2ccccc2)cc2cc([N+](=O)[O-])ccc21\tCHEMBL4216885\n",
      "Cc1ccccc1-c1cc2cc([N+](=O)[O-])ccc2[nH]1\tCHEMBL4212655\n",
      "Clc1ccc2[nH]c(-c3ccccc3)cc2c1\tCHEMBL1424243\n",
      "N#Cc1ccc2[nH]c(-c3ccccc3)cc2c1\tCHEMBL509962\n",
      "Brc1ccc2[nH]c(-c3ccccc3)cc2c1\tCHEMBL4204862\n",
      "N#Cc1ccc2[nH]c(-c3cccnc3)cc2c1\tCHEMBL4207850\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL1444\n",
      "N#Cc1ccc([C@H]2CCc3cncn32)c(F)c1\tCHEMBL3099695\n",
      "N#Cc1ccc([C@H]2CCCc3cncn32)cc1\tCHEMBL287677\n",
      "CN1C(=O)CCc2cc(-c3cccnc3)ccc21\tCHEMBL446083\n",
      "CC1(C)CC(=O)c2c(noc2Cc2ccccc2)C1\tCHEMBL4207995\n",
      "COc1ccc(Cc2onc3c2C(=O)CC(C)(C)C3)cc1\tCHEMBL4212612\n",
      "CC1(C)CC(=O)c2c(noc2Cc2ccc(C(N)=O)cc2)C1\tCHEMBL4211193\n",
      "CN1CCN(C(=O)c2ccc(Cc3onc4c3C(=O)CC(C)(C)C4)cc2)CC1=O\tCHEMBL4212918\n",
      "CN(CCO)C(=O)c1ccc(Cc2onc3c2C(=O)CC(C)(C)C3)cc1\tCHEMBL4214618\n",
      "CC1(C)CC(=O)c2c(noc2Cc2ccc(C(=O)N3CC[C@@H](O)C3)cc2)C1\tCHEMBL4207348\n",
      "Oc1ccc(/C=C/c2cc(O)cc(O)c2)cc1\tCHEMBL165\n",
      "c1ccc(-c2nsc(-c3ccccc3)n2)cc1\tCHEMBL264401\n",
      "c1cncc(-c2nsc(-c3cccnc3)n2)c1\tCHEMBL1939366\n",
      "c1cncc(-c2csc(-c3cccnc3)n2)c1\tCHEMBL118266\n",
      "CC(C)(C#N)c1cc(Cn2cncn2)cc(C(C)(C)C#N)c1\tCHEMBL1399\n",
      "Oc1ccccc1-c1csc(-c2ncccn2)n1\tCHEMBL4227385\n",
      "Oc1cccc(-c2csc(-c3ncccn3)n2)c1\tCHEMBL4227979\n",
      "Oc1ccc(-c2csc(-c3ncccn3)n2)cc1\tCHEMBL4227126\n",
      "Cc1ccc(O)c(-c2csc(-c3ncccn3)n2)c1\tCHEMBL4228236\n",
      "COc1ccc(O)c(-c2csc(-c3ncccn3)n2)c1\tCHEMBL4229074\n",
      "Oc1ccc(Cl)cc1-c1csc(-c2ncccn2)n1\tCHEMBL4228495\n",
      "COc1ccccc1-c1csc(-c2ncccn2)n1\tCHEMBL4224728\n",
      "COc1cccc(-c2csc(-c3ncccn3)n2)c1\tCHEMBL4228753\n",
      "COc1ccc(-c2csc(-c3ncccn3)n2)cc1\tCHEMBL4224993\n",
      "Clc1ccccc1-c1csc(-c2ncccn2)n1\tCHEMBL4229012\n",
      "Clc1cccc(-c2csc(-c3ncccn3)n2)c1\tCHEMBL4225257\n",
      "Clc1ccc(-c2csc(-c3ncccn3)n2)cc1\tCHEMBL4229272\n",
      "c1ccc(-c2csc(-c3ncccn3)n2)nc1\tCHEMBL4225521\n",
      "c1cnc(-c2nc(-c3cccnc3)cs2)nc1\tCHEMBL4228303\n",
      "c1cnc(-c2nc(-c3ccncc3)cs2)nc1\tCHEMBL4227458\n",
      "c1cnc(-c2nc(-c3ccco3)cs2)nc1\tCHEMBL4224875\n",
      "c1cnc(-c2nc(-c3cccs3)cs2)nc1\tCHEMBL4227725\n",
      "c1cnc(-c2nc(-c3cc4ccccc4o3)cs2)nc1\tCHEMBL4228819\n",
      "c1cnc(-c2nc(-c3cc4ccccc4s3)cs2)nc1\tCHEMBL4225998\n",
      "Cn1c(-c2csc(-c3ncccn3)n2)nc2ccccc21\tCHEMBL4228031\n",
      "c1cnc(-c2nc(-c3nc4ccccc4s3)cs2)nc1\tCHEMBL4228244\n",
      "CC(=O)N1CCN(c2ccc(OC[C@H]3CO[C@](Cn4ccnc4)(c4ccc(Cl)cc4Cl)O3)cc2)CC1\tCHEMBL75\n",
      "CC(=O)N1CCN(c2ccc(/C=C/c3cc(F)cc(-c4ccncc4)c3)cc2)CC1\tCHEMBL4129721\n",
      "O=S(=O)(C1CC1)N1CCN(c2ccc(/C=C/c3cc(F)cc(-c4ccncc4)c3)cc2)CC1\tCHEMBL4126996\n",
      "O=S(=O)(c1cccc(OC(F)(F)F)c1)N1CCN(c2ccc(/C=C/c3cc(Cl)cc(Cn4ccnc4)c3)cc2)CC1\tCHEMBL4288256\n",
      "CCS(=O)(=O)N1CCN(c2ccc(/C=C/c3cc(F)cc(-c4ccncc4)c3)cc2)CC1\tCHEMBL4126284\n",
      "O=S(=O)(N1CCN(c2ccc(/C=C/c3cc(F)cc(-c4ccncc4)c3)cc2)CC1)C(F)(F)F\tCHEMBL4128368\n",
      "CC(=O)N1CCN(c2ccc(CO[C@H]3CO[C@](Cn4ccnc4)(c4ccc(Cl)cc4Cl)OC3)cc2)CC1\tCHEMBL4289930\n",
      "Fc1cc2nc(-c3cncnc3C(F)(F)F)n(C3CC3)c2cc1F\tCHEMBL4439573\n",
      "Cc1ccc(S(=O)(=O)Nc2ccc3cc[nH]c3c2)cc1\tCHEMBL4516755\n",
      "O=S(=O)(Cc1ccccc1)Nc1ccc2[nH]ccc2c1\tCHEMBL4537791\n",
      "O=S(=O)(Cc1ccccc1)NCCc1c[nH]c2ccccc12\tCHEMBL4464506\n",
      "O=S(=O)(NCCc1c[nH]c2ccccc12)c1ccccc1\tCHEMBL1371590\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL1444\n",
      "Cc1[nH]c2ccccc2c1/C=N\\Nc1ccc(Cl)cc1\tCHEMBL4564431\n",
      "Cc1ccc(S(=O)(=O)NCc2cccc(CNS(=O)(=O)c3ccc(C)cc3)c2)cc1\tCHEMBL4449929\n",
      "Cc1cc(C)c(C)c(S(=O)(=O)NCc2cccc(CNS(=O)(=O)c3c(C)c(C)cc(C)c3C)c2)c1C\tCHEMBL4436270\n",
      "O=S(=O)(NCc1cccc(CNS(=O)(=O)c2ccc3ccccc3c2)c1)c1ccc2ccccc2c1\tCHEMBL4462178\n",
      "COc1ccc(S(=O)(=O)NCc2cccc(CNS(=O)(=O)c3ccc(OC)cc3)c2)cc1\tCHEMBL4531367\n",
      "O=S(=O)(NCc1cccc(CNS(=O)(=O)c2ccc(C(F)(F)F)cc2)c1)c1ccc(C(F)(F)F)cc1\tCHEMBL4556427\n",
      "O=S(=O)(NCc1cccc(CNS(=O)(=O)c2ccc(F)cc2)c1)c1ccc(F)cc1\tCHEMBL4536389\n",
      "O=S(=O)(NCc1cccc(CNS(=O)(=O)c2ccc(Cl)cc2)c1)c1ccc(Cl)cc1\tCHEMBL4460718\n",
      "O=S(=O)(NCc1cccc(CNS(=O)(=O)c2ccc(Br)cc2)c1)c1ccc(Br)cc1\tCHEMBL4590541\n",
      "N#Cc1ccc(S(=O)(=O)NCc2cccc(CNS(=O)(=O)c3ccc(C#N)cc3)c2)cc1\tCHEMBL4444996\n",
      "CC(=O)c1ccc(S(=O)(=O)NCc2cccc(CNS(=O)(=O)c3ccc(C(C)=O)cc3)c2)cc1\tCHEMBL4567479\n",
      "S=C(NCc1cccc(CNC(=S)Nc2ccc(Cl)cc2)c1)Nc1ccc(Cl)cc1\tCHEMBL2299369\n",
      "O=[N+]([O-])c1ccccc1S(=O)(=O)NCc1cccc(CNS(=O)(=O)c2ccccc2[N+](=O)[O-])c1\tCHEMBL4555477\n",
      "O=[N+]([O-])c1cccc(S(=O)(=O)NCc2cccc(CNS(=O)(=O)c3cccc([N+](=O)[O-])c3)c2)c1\tCHEMBL4439561\n",
      "Nc1cccc(S(=O)(=O)NCc2cccc(CNS(=O)(=O)c3cccc(N)c3)c2)c1\tCHEMBL4448018\n",
      "Cc1cc(C)c(C)c(S(=O)(=O)NCc2ccc(CNS(=O)(=O)c3c(C)c(C)cc(C)c3C)cc2)c1C\tCHEMBL4585518\n",
      "N#Cc1ccc(S(=O)(=O)NCc2ccc(CNS(=O)(=O)c3ccc(C#N)cc3)cc2)cc1\tCHEMBL4527809\n",
      "COc1ccc(S(=O)(=O)Nc2cccc(NS(=O)(=O)c3ccc(OC)cc3)c2)cc1\tCHEMBL4543747\n",
      "O=S(=O)(Nc1cccc(NS(=O)(=O)c2ccc(Cl)cc2)c1)c1ccc(Cl)cc1\tCHEMBL4446561\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL1444\n",
      "O=[N+]([O-])c1ccc(S(=O)(=O)NCc2cccc(CNS(=O)(=O)c3ccc([N+](=O)[O-])cc3)c2)cc1\tCHEMBL4570681\n",
      "O=[N+]([O-])c1ccc(NC(=S)NCc2cccc(CNC(=S)Nc3ccc([N+](=O)[O-])cc3)c2)cc1\tCHEMBL2299368\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL1444\n",
      "CC/C(=C(\\c1ccc(O)cc1)c1ccc(OCCNC)cc1)c1ccccc1\tCHEMBL1093458\n",
      "CC[C@H](Nc1cc(C)nc(Nc2cccc(O)c2)n1)c1nccs1\tCHEMBL4437198\n",
      "O=C1c2c(O)ccc(O)c2C(=O)c2c(NCCCO)ccc(NCCCO)c21\tCHEMBL4568316\n",
      "N[C@@H]1CCN(Cc2cc(O)c3cc(F)ccc3n2)C[C@H]1O\tCHEMBL4474808\n",
      "COC(=O)c1ccc(NCC2=CC(=O)c3ccccc3C2=O)cc1\tCHEMBL1991089\n",
      "O=C(CCc1ccc(O)c(Cl)c1)NCc1csc(=O)[nH]1\tCHEMBL4453737\n",
      "C=CC[C@H]1C[C@H]2[C@@H]3CCC(=O)[C@@]3(C)CC[C@@H]2[C@@]2(C)CCC(=O)C=C12\tCHEMBL4514728\n",
      "C=CC[C@H]1C[C@H]2[C@@H]3CCC(=O)[C@@]3(C)CC[C@@H]2[C@@]2(C)CCC(=O)C(O)=C12\tCHEMBL4468497\n",
      "C[C@@H]1CC2=CC(=O)CC[C@]2(C)[C@H]2CC[C@]3(C)C(=O)CC[C@H]3[C@@H]21\tCHEMBL487113\n",
      "C[C@H]1C[C@H]2[C@@H]3CCC(=O)[C@@]3(C)CC[C@@H]2[C@@]2(C)CCC(=O)C=C12\tCHEMBL3754471\n",
      "C[C@@H]1CC2=CCCC[C@]2(C)[C@H]2CC[C@]3(C)C(=O)CC[C@H]3[C@@H]21\tCHEMBL4451962\n",
      "C[C@H]1C[C@H]2[C@@H]3CCC(=O)[C@@]3(C)CC[C@@H]2[C@@]2(C)CCCC=C12\tCHEMBL3751881\n",
      "C[C@H]1C[C@H]2[C@@H]3CCC(=O)[C@@]3(C)CC[C@@H]2[C@@]2(C)CC[C@H]3O[C@H]3[C@H]12\tCHEMBL4460869\n",
      "C[C@H]1C[C@H]2[C@@H]3CCC(=O)[C@@]3(C)CC[C@@H]2[C@@]2(C)CCC=C[C@H]12\tCHEMBL4564333\n",
      "C=CC[C@H]1C[C@@H]2[C@H](CC[C@]3(C)C(=O)CC[C@@H]23)[C@@]2(C)C=CC(=O)C=C12\tCHEMBL4575994\n",
      "C=CC[C@@H]1CC2=C(O)C(=O)CC[C@]2(C)[C@H]2CC[C@]3(C)C(=O)CC[C@H]3[C@@H]21\tCHEMBL4454575\n",
      "C=CC[C@H]1C[C@H]2[C@@H]3CCC(=O)[C@@]3(C)CC[C@@H]2[C@@]2(C)CCCC=C12\tCHEMBL4520056\n",
      "C[C@]12CC[C@H]3[C@@H](C[C@@H](O)C4=CC(=O)CC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL4515880\n",
      "C[C@]12CC[C@H]3[C@@H](C[C@H](O)C4=CC(=O)CC[C@@]43C)[C@@H]1CCC2=O\tCHEMBL4520027\n",
      "CCCCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL29863\n",
      "CC(C)CC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL28669\n",
      "CCC(C)C1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL28668\n",
      "CCCCCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL25735\n",
      "CC(C)CCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL29230\n",
      "CCC(C)CC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL287647\n",
      "CCCC(C)C1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL29521\n",
      "CCCCCCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL29339\n",
      "CCCCCCCC1(c2ccc(N)cc2)CCC(=O)NC1=O\tCHEMBL29338\n",
      "N#Cc1ccc(Cn2ccnc2)c2oc3ccccc3c(=O)c12\tCHEMBL421149\n",
      "O=c1c2ccccc2oc2c(Cn3ccnc3)ccc([N+](=O)[O-])c12\tCHEMBL159073\n",
      "C=C(c1ccc(C#N)cc1)n1ccnc1\tCHEMBL31760\n",
      "O=C1/C(=C/c2ccncc2)C(c2ccccc2)Oc2ccccc21\tCHEMBL10026\n",
      "COc1ccc2c(c1)OC(c1ccccc1)/C(=C\\c1ccncc1)C2=O\tCHEMBL10137\n",
      "COc1ccc2c(c1)O[C@@H](c1ccccc1)C[C@@H]2n1ccnc1\tCHEMBL4583456\n",
      "Oc1ccc2c(c1)O[C@@H](c1ccccc1)C[C@@H]2n1ccnc1\tCHEMBL4540847\n",
      "N#Cc1ccc(N(CCSc2ccc(O)c(Cl)c2)n2cnnc2)cc1\tCHEMBL3622055\n",
      "N#Cc1ccc(N(Cc2ccc(OS(N)(=O)=O)c(Cl)c2)n2cnnc2)cc1\tCHEMBL322322\n",
      "N#Cc1ccc(N(Cc2ccc(OS(N)(=O)=O)c(Br)c2)n2cnnc2)cc1\tCHEMBL108483\n",
      "N#Cc1ccc(N(Cc2ccc3c(Br)c(OS(N)(=O)=O)ccc3c2)n2cnnc2)cc1\tCHEMBL3622064\n",
      "N#Cc1ccc(N(Cc2ccc3c(Cl)c(OS(N)(=O)=O)ccc3c2)n2cnnc2)cc1\tCHEMBL3622063\n",
      "N#Cc1ccc(N(Cc2ccc(OS(N)(=O)=O)cc2F)n2cnnc2)cc1\tCHEMBL4465348\n",
      "COc1ccc2c(c1)C(N)=C(Cc1ccncc1)C2=O\tCHEMBL1997442\n",
      "O=c1c(-c2ccccc2)c(SCc2ccncc2)oc2cc(OCc3ccccc3)ccc12\tCHEMBL188664\n",
      "N#Cc1ccc(N(Cc2ccc(Br)cc2)n2cnnc2)cc1\tCHEMBL108425\n",
      "O=[N+]([O-])c1ccc(N(Cc2ccc(Br)cc2)n2cnnc2)cc1\tCHEMBL4520995\n",
      "C[C@]12CC[C@H]3[C@@H](CC[C@H]4[C@@H]5O[C@@H]5CC[C@]34C)[C@@H]1CC[C@@H]2O\tCHEMBL1629803\n",
      "N#Cc1ccc(N(Cc2ccc3c(Br)c(OS(N)(=O)=O)ccc3c2)n2cnnc2)cc1\tCHEMBL3622064\n",
      "N#Cc1ccc(N(Cc2ccc3c(Cl)c(OS(N)(=O)=O)ccc3c2)n2cnnc2)cc1\tCHEMBL3622063\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL1444\n",
      "O=[N+]([O-])c1cccc(CN(c2ccc([N+](=O)[O-])c(-c3ccccc3)c2)n2cnnc2)c1\tCHEMBL4563511\n",
      "N#Cc1cccc(CN(c2ccc([N+](=O)[O-])c(-c3ccccc3)c2)n2cnnc2)c1\tCHEMBL4546916\n",
      "O=[N+]([O-])c1ccc(N(Cc2cccc(C(F)(F)F)c2)n2cnnc2)cc1-c1ccccc1\tCHEMBL4556775\n",
      "O=[N+]([O-])c1ccc(N(Cc2ccc(C(F)(F)F)cc2)n2cnnc2)cc1-c1ccccc1\tCHEMBL4560281\n",
      "O=[N+]([O-])c1ccc(N(Cc2ccccc2)n2cnnc2)cc1-c1ccccc1\tCHEMBL4592407\n",
      "O=[N+]([O-])c1ccc(N(Cc2ccc(Cl)cc2)n2cnnc2)cc1Br\tCHEMBL4574913\n",
      "O=[N+]([O-])c1ccc(N(Cc2cccc(C(F)(F)F)c2)n2cnnc2)cc1Br\tCHEMBL4514474\n",
      "O=[N+]([O-])c1ccc(N(Cc2ccc(C(F)(F)F)cc2)n2cnnc2)cc1Br\tCHEMBL4526798\n",
      "N#Cc1ccccc1CN(c1ccc([N+](=O)[O-])c(Br)c1)n1cnnc1\tCHEMBL4564168\n",
      "N#Cc1cccc(CN(c2ccc([N+](=O)[O-])c(Br)c2)n2cnnc2)c1\tCHEMBL4568674\n",
      "O=[N+]([O-])c1cccc(CN(c2ccc([N+](=O)[O-])c(Br)c2)n2cnnc2)c1\tCHEMBL4575939\n",
      "O=[N+]([O-])c1ccc(N(Cc2ccccc2)n2cnnc2)cc1Br\tCHEMBL4536893\n",
      "O=[N+]([O-])c1ccc(Nn2cnnc2)cc1Br\tCHEMBL4547558\n",
      "O=[N+]([O-])c1ccc(Nn2cnnc2)cc1Br\tCHEMBL4547558\n",
      "O=[N+]([O-])c1ccc(N(Cc2ccccc2)n2cnnc2)cc1Br\tCHEMBL4536893\n",
      "O=[N+]([O-])c1cccc(CN(c2ccc([N+](=O)[O-])c(Br)c2)n2cnnc2)c1\tCHEMBL4575939\n",
      "N#Cc1cccc(CN(c2ccc([N+](=O)[O-])c(Br)c2)n2cnnc2)c1\tCHEMBL4568674\n",
      "N#Cc1ccccc1CN(c1ccc([N+](=O)[O-])c(Br)c1)n1cnnc1\tCHEMBL4564168\n",
      "O=[N+]([O-])c1ccc(N(Cc2ccc(C(F)(F)F)cc2)n2cnnc2)cc1Br\tCHEMBL4526798\n",
      "O=[N+]([O-])c1ccc(N(Cc2cccc(C(F)(F)F)c2)n2cnnc2)cc1Br\tCHEMBL4514474\n",
      "O=[N+]([O-])c1ccc(N(Cc2ccc(Cl)cc2)n2cnnc2)cc1Br\tCHEMBL4574913\n",
      "O=[N+]([O-])c1ccc(N(Cc2ccccc2)n2cnnc2)cc1-c1ccccc1\tCHEMBL4592407\n",
      "O=[N+]([O-])c1ccc(N(Cc2ccc(C(F)(F)F)cc2)n2cnnc2)cc1-c1ccccc1\tCHEMBL4560281\n",
      "O=[N+]([O-])c1ccc(N(Cc2cccc(C(F)(F)F)c2)n2cnnc2)cc1-c1ccccc1\tCHEMBL4556775\n",
      "N#Cc1cccc(CN(c2ccc([N+](=O)[O-])c(-c3ccccc3)c2)n2cnnc2)c1\tCHEMBL4546916\n",
      "O=[N+]([O-])c1cccc(CN(c2ccc([N+](=O)[O-])c(-c3ccccc3)c2)n2cnnc2)c1\tCHEMBL4563511\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL1444\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL1444\n",
      "CCC#CCOc1cc(Cn2ccnc2)c2oc3ccccc3c(=O)c2c1\tCHEMBL4634777\n",
      "CCC#CCOc1cc(Cn2ccnc2)c2c(=O)c3ccccc3oc2c1\tCHEMBL4639677\n",
      "CCC#CCOc1ccc2c(=O)c3ccc(Cn4ccnc4)cc3oc2c1\tCHEMBL4632445\n",
      "CCC(=C(c1ccc(O)cc1)c1ccc(O)cc1)c1ccccc1\tCHEMBL149791\n",
      "N#CCC(=C(c1ccc(O)cc1)c1ccc(O)cc1)c1ccccc1\tCHEMBL4763409\n",
      "Oc1ccc(C(=C(Cn2ccnc2)c2ccccc2)c2ccc(O)cc2)cc1\tCHEMBL4777271\n",
      "Oc1ccc(C(=C(Cn2cncn2)c2ccccc2)c2ccc(O)cc2)cc1\tCHEMBL4797578\n",
      "N#CCC(=C(c1ccc(O)cc1)c1ccc(O)cc1)c1ccc(O)cc1\tCHEMBL4747849\n",
      "Oc1ccc(C(Cn2ccnc2)=C(c2ccc(O)cc2)c2ccc(O)cc2)cc1\tCHEMBL4780337\n",
      "Oc1ccc(C(Cn2cncn2)=C(c2ccc(O)cc2)c2ccc(O)cc2)cc1\tCHEMBL4753984\n",
      "Oc1ccc(C(Cn2cnnc2)=C(c2ccc(O)cc2)c2ccc(O)cc2)cc1\tCHEMBL4749692\n",
      "N#CCC(=C(c1ccc(O)cc1)c1ccc(O)cc1)c1ccc(O)c(F)c1\tCHEMBL4743255\n",
      "Oc1ccc(C(=C(Cn2ccnc2)c2ccc(O)c(F)c2)c2ccc(O)cc2)cc1\tCHEMBL4753594\n",
      "Oc1ccc(C(=C(Cn2cncn2)c2ccc(O)c(F)c2)c2ccc(O)cc2)cc1\tCHEMBL4764457\n",
      "Oc1ccc(C(=C(Cn2cnnc2)c2ccc(O)c(F)c2)c2ccc(O)cc2)cc1\tCHEMBL4752568\n",
      "N#CCC(=C(c1ccc(O)cc1)c1ccc(O)cc1)c1ccc(N)cc1\tCHEMBL4760286\n",
      "Nc1ccc(C(Cn2ccnc2)=C(c2ccc(O)cc2)c2ccc(O)cc2)cc1\tCHEMBL4755831\n",
      "Nc1ccc(C(Cn2cncn2)=C(c2ccc(O)cc2)c2ccc(O)cc2)cc1\tCHEMBL4750835\n",
      "Nc1ccc(C(Cn2cnnc2)=C(c2ccc(O)cc2)c2ccc(O)cc2)cc1\tCHEMBL4745681\n",
      "Oc1ccc(C(=C(Cn2ccnc2)c2cccc(O)c2)c2ccc(O)cc2)cc1\tCHEMBL4778401\n",
      "Nc1cccc(C(Cn2ccnc2)=C(c2ccc(O)cc2)c2ccc(O)cc2)c1\tCHEMBL4761359\n",
      "O=C(NCc1ccccc1)OC(Cn1ccnc1)c1ccccc1\tCHEMBL4858552\n",
      "O=C(Nc1ccc(Cl)cc1)OC(COc1ccccc1)Cn1ccnc1\tCHEMBL4874326\n",
      "N#Cc1ccc(S(=O)(=O)Nc2ccc(/N=N/c3ccccc3)cc2)cc1\tCHEMBL4847316\n",
      "COc1ccc(S(=O)(=O)Nc2ccc(/N=N/c3ccccc3)cc2)c(OC)c1\tCHEMBL4870604\n",
      "COc1ccc(S(=O)(=O)N(Cc2nc3ccccc3[nH]2)c2ccc(/N=N/c3ccccc3)cc2)c(OC)c1\tCHEMBL4868867\n",
      "COc1ccc(S(=O)(=O)N2CCCC(Cn3ccnc3)C2)cc1C(C)(C)C\tCHEMBL1784801\n",
      "Oc1ccc(/C=C/c2cc(O)cc(O)c2)cc1\tCHEMBL165\n",
      "Nc1ccc(/C=C/c2ccccc2)cc1\tCHEMBL69011\n",
      "COc1cnc(NS(=O)(=O)c2ccc(N/C=C\\C(=O)c3cc4ccccc4oc3=O)cc2)nc1\tCHEMBL3924170\n",
      "CC(C)c1cc(O)c2c(c1)[C@H](O)C[C@H]1C(C)(C)CCC[C@]21C\tCHEMBL4875917\n",
      "C[C@H]1C[C@]2(O)O[C@@]1(C)C=C1CC[C@H]3C(C)(C)CCC[C@]3(C)[C@H]12\tCHEMBL4866633\n",
      "C[C@@H]1O[C@]2(C(C)(C)O)C=C3CC[C@H]4C(C)(C)CCC[C@]4(C)[C@@]31OO2\tCHEMBL4859743\n",
      "C=C[C@]1(C)C=C2CC[C@H]3C(C)(C)CCC[C@]3(C)[C@H]2CC1\tCHEMBL4863718\n",
      "C=C[C@@]1(C)CC(=O)C2=C(CC[C@H]3C(C)(C)CCC[C@]23C)C1\tCHEMBL4874928\n",
      "CC(C)c1cc(O)c2c(c1)CC[C@H]1C(C)(C)CCC[C@]21C\tCHEMBL4852023\n",
      "CC(=O)N1CCN(c2ccc(OC[C@H]3CO[C@](Cn4ccnc4)(c4ccc(Cl)cc4Cl)O3)cc2)CC1\tCHEMBL75\n",
      "C=C1C[C@@H]2[C@H](CC[C@]3(C)C(=O)CC[C@@H]23)[C@@]2(C)C=CC(=O)C=C12\tCHEMBL1200374\n",
      "N#Cc1ccc(C(c2ccc(C#N)cc2)n2cncn2)cc1\tCHEMBL1444\n"
     ]
    }
   ],
   "source": [
    "!type molecule.smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('padel.sh', <http.client.HTTPMessage at 0x15d57ccc648>)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#using file from https://github.com/dataprofessor/bioinformatics/raw/master/padel.sh\n",
    "import urllib.request\n",
    "url = 'https://github.com/dataprofessor/bioinformatics/raw/master/padel.sh'\n",
    "filename = 'padel.sh'\n",
    "urllib.request.urlretrieve(url, filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing CHEMBL341591 in molecule.smi (1/2806). \n",
      "Processing CHEMBL2111947 in molecule.smi (2/2806). \n",
      "Processing CHEMBL431859 in molecule.smi (3/2806). \n",
      "Processing CHEMBL112021 in molecule.smi (5/2806). \n",
      "Processing CHEMBL113637 in molecule.smi (4/2806). \n",
      "Processing CHEMBL324070 in molecule.smi (6/2806). \n",
      "Processing CHEMBL41761 in molecule.smi (7/2806). \n",
      "Processing CHEMBL111868 in molecule.smi (8/2806). \n",
      "Processing CHEMBL111888 in molecule.smi (9/2806). \n",
      "Processing CHEMBL112074 in molecule.smi (10/2806). \n",
      "Processing CHEMBL324326 in molecule.smi (11/2806). \n",
      "Processing CHEMBL37321 in molecule.smi (12/2806). \n",
      "Processing CHEMBL353068 in molecule.smi (13/2806). \n",
      "Processing CHEMBL41066 in molecule.smi (14/2806). \n",
      "Processing CHEMBL166709 in molecule.smi (15/2806). \n",
      "Processing CHEMBL424556 in molecule.smi (16/2806). \n",
      "Processing CHEMBL1630273 in molecule.smi (17/2806). Average speed: 2.12 s/mol.\n",
      "Processing CHEMBL1630261 in molecule.smi (18/2806). Average speed: 2.17 s/mol.\n",
      "Processing CHEMBL169251 in molecule.smi (19/2806). Average speed: 0.74 s/mol.\n",
      "Processing CHEMBL168636 in molecule.smi (20/2806). Average speed: 0.56 s/mol.\n",
      "Processing CHEMBL90585 in molecule.smi (21/2806). Average speed: 0.47 s/mol.\n",
      "Processing CHEMBL433728 in molecule.smi (23/2806). Average speed: 0.41 s/mol.\n",
      "Processing CHEMBL1629805 in molecule.smi (22/2806). Average speed: 0.41 s/mol.\n",
      "Processing CHEMBL38877 in molecule.smi (24/2806). Average speed: 0.31 s/mol.\n",
      "Processing CHEMBL169449 in molecule.smi (25/2806). Average speed: 0.28 s/mol.\n",
      "Processing CHEMBL39275 in molecule.smi (26/2806). Average speed: 0.25 s/mol.\n",
      "Processing CHEMBL39513 in molecule.smi (27/2806). Average speed: 0.23 s/mol.\n",
      "Processing CHEMBL2112738 in molecule.smi (28/2806). Average speed: 0.21 s/mol.\n",
      "Processing CHEMBL289116 in molecule.smi (29/2806). Average speed: 0.20 s/mol.\n",
      "Processing CHEMBL289116 in molecule.smi (30/2806). Average speed: 0.21 s/mol.\n",
      "Processing CHEMBL39782 in molecule.smi (31/2806). Average speed: 0.20 s/mol.\n",
      "Processing CHEMBL304903 in molecule.smi (32/2806). Average speed: 0.19 s/mol.\n",
      "Processing CHEMBL488 in molecule.smi (33/2806). Average speed: 0.18 s/mol.\n",
      "Processing CHEMBL488 in molecule.smi (34/2806). Average speed: 0.18 s/mol.\n",
      "Processing CHEMBL168434 in molecule.smi (35/2806). Average speed: 0.16 s/mol.\n",
      "Processing CHEMBL352645 in molecule.smi (36/2806). Average speed: 0.16 s/mol.\n",
      "Processing CHEMBL2112739 in molecule.smi (37/2806). Average speed: 0.15 s/mol.\n",
      "Processing CHEMBL440930 in molecule.smi (38/2806). Average speed: 0.14 s/mol.\n",
      "Processing CHEMBL1630274 in molecule.smi (39/2806). Average speed: 0.14 s/mol.\n",
      "Processing CHEMBL1629804 in molecule.smi (40/2806). Average speed: 0.14 s/mol.\n",
      "Processing CHEMBL39661 in molecule.smi (41/2806). Average speed: 0.13 s/mol.\n",
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      "Processing CHEMBL491825 in molecule.smi (1241/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL365120 in molecule.smi (1475/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL175554 in molecule.smi (1502/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL366773 in molecule.smi (1504/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL175799 in molecule.smi (1503/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL175609 in molecule.smi (1506/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL179222 in molecule.smi (1505/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL175979 in molecule.smi (1507/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL193496 in molecule.smi (1508/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL365314 in molecule.smi (1509/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL193858 in molecule.smi (1511/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL370281 in molecule.smi (1510/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL371742 in molecule.smi (1512/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL192077 in molecule.smi (1513/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL192155 in molecule.smi (1514/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL370530 in molecule.smi (1517/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL363849 in molecule.smi (1518/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL362441 in molecule.smi (1536/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL366570 in molecule.smi (1542/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL176009 in molecule.smi (1544/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL366490 in molecule.smi (1545/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL176076 in molecule.smi (1546/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL360026 in molecule.smi (1551/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL175554 in molecule.smi (1554/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL366773 in molecule.smi (1556/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL175799 in molecule.smi (1555/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL179222 in molecule.smi (1557/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL175609 in molecule.smi (1558/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL175979 in molecule.smi (1559/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL193496 in molecule.smi (1560/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL365314 in molecule.smi (1561/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL370281 in molecule.smi (1562/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL193858 in molecule.smi (1563/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL192077 in molecule.smi (1565/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL1630280 in molecule.smi (1604/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL488513 in molecule.smi (1609/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL157101 in molecule.smi (1615/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL437731 in molecule.smi (1643/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL1688904 in molecule.smi (1646/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL1688906 in molecule.smi (1648/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL1688912 in molecule.smi (1654/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL225472 in molecule.smi (1656/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL1688914 in molecule.smi (1657/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1688915 in molecule.smi (1658/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL387892 in molecule.smi (1662/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL1689257 in molecule.smi (1665/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1689258 in molecule.smi (1666/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1689259 in molecule.smi (1667/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL518414 in molecule.smi (1668/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL1444 in molecule.smi (1670/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL404225 in molecule.smi (1672/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL488 in molecule.smi (1671/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL256665 in molecule.smi (1674/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1783176 in molecule.smi (1675/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1783178 in molecule.smi (1676/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL1783177 in molecule.smi (1679/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL95109 in molecule.smi (1680/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL95109 in molecule.smi (1681/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1444 in molecule.smi (1683/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL95109 in molecule.smi (1682/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1784788 in molecule.smi (1684/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1485809 in molecule.smi (1685/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL1784791 in molecule.smi (1688/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL1784793 in molecule.smi (1690/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1784794 in molecule.smi (1691/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1784795 in molecule.smi (1692/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1784796 in molecule.smi (1693/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1784800 in molecule.smi (1696/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1784797 in molecule.smi (1694/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1784798 in molecule.smi (1695/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1784801 in molecule.smi (1697/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1784803 in molecule.smi (1698/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1784804 in molecule.smi (1699/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1784805 in molecule.smi (1700/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1521315 in molecule.smi (1701/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1389987 in molecule.smi (1702/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1784806 in molecule.smi (1703/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1303900 in molecule.smi (1704/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1784807 in molecule.smi (1705/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1351273 in molecule.smi (1706/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1784808 in molecule.smi (1707/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1538978 in molecule.smi (1708/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1784809 in molecule.smi (1709/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1784810 in molecule.smi (1710/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL224789 in molecule.smi (1714/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1824742 in molecule.smi (1715/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1824743 in molecule.smi (1716/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1824744 in molecule.smi (1717/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1824745 in molecule.smi (1718/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1824747 in molecule.smi (1720/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1824746 in molecule.smi (1719/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1824749 in molecule.smi (1722/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1824748 in molecule.smi (1721/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1824750 in molecule.smi (1723/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1824751 in molecule.smi (1724/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1824752 in molecule.smi (1725/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1824753 in molecule.smi (1726/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1824754 in molecule.smi (1727/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1824755 in molecule.smi (1728/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1824756 in molecule.smi (1729/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1824757 in molecule.smi (1730/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1824758 in molecule.smi (1731/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1824759 in molecule.smi (1732/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1824760 in molecule.smi (1733/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1824761 in molecule.smi (1734/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1824762 in molecule.smi (1735/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1824763 in molecule.smi (1736/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1824764 in molecule.smi (1737/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1824765 in molecule.smi (1738/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1824766 in molecule.smi (1739/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1824767 in molecule.smi (1740/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1824768 in molecule.smi (1741/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1824769 in molecule.smi (1742/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1825018 in molecule.smi (1744/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1825017 in molecule.smi (1743/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1825019 in molecule.smi (1745/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1825020 in molecule.smi (1746/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL349822 in molecule.smi (1747/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1825021 in molecule.smi (1748/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL14192 in molecule.smi (1749/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1825022 in molecule.smi (1750/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1444 in molecule.smi (1751/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL29482 in molecule.smi (1752/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1825023 in molecule.smi (1753/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1825024 in molecule.smi (1754/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL336638 in molecule.smi (1755/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1825025 in molecule.smi (1756/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1825026 in molecule.smi (1757/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1825027 in molecule.smi (1758/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL1825031 in molecule.smi (1762/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL1825035 in molecule.smi (1766/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL1825036 in molecule.smi (1767/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL1825040 in molecule.smi (1771/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1825041 in molecule.smi (1772/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1825042 in molecule.smi (1773/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1825043 in molecule.smi (1774/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL272339 in molecule.smi (1775/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1825044 in molecule.smi (1776/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL1825046 in molecule.smi (1778/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1825047 in molecule.smi (1779/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1825049 in molecule.smi (1781/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1825048 in molecule.smi (1780/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1098737 in molecule.smi (1782/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL9165 in molecule.smi (1783/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1915151 in molecule.smi (1784/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL1915156 in molecule.smi (1786/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1915159 in molecule.smi (1787/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1915161 in molecule.smi (1788/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1915167 in molecule.smi (1789/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1915176 in molecule.smi (1790/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1915401 in molecule.smi (1791/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1915403 in molecule.smi (1792/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL9352 in molecule.smi (1793/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL264401 in molecule.smi (1794/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1939366 in molecule.smi (1795/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1454842 in molecule.smi (1796/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1939519 in molecule.smi (1797/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1939520 in molecule.smi (1798/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL1939523 in molecule.smi (1801/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL1939525 in molecule.smi (1803/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL1939528 in molecule.smi (1806/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL1939532 in molecule.smi (1810/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1939533 in molecule.smi (1811/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1939534 in molecule.smi (1812/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL1939536 in molecule.smi (1814/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1939537 in molecule.smi (1815/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1939538 in molecule.smi (1816/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1939539 in molecule.smi (1817/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1939540 in molecule.smi (1818/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1939541 in molecule.smi (1819/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL1939543 in molecule.smi (1821/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1939544 in molecule.smi (1822/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL1939547 in molecule.smi (1825/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1939548 in molecule.smi (1826/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL1939550 in molecule.smi (1828/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1939551 in molecule.smi (1829/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL503399 in molecule.smi (1830/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1939552 in molecule.smi (1831/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL165 in molecule.smi (1832/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL87333 in molecule.smi (1833/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL111234 in molecule.smi (1834/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL9352 in molecule.smi (1835/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1956171 in molecule.smi (1836/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1728910 in molecule.smi (1837/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1956175 in molecule.smi (1838/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1956179 in molecule.smi (1839/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1956180 in molecule.smi (1840/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1955869 in molecule.smi (1841/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1956183 in molecule.smi (1842/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1956186 in molecule.smi (1843/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1956188 in molecule.smi (1844/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1957208 in molecule.smi (1846/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1444 in molecule.smi (1845/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1957209 in molecule.smi (1847/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1601919 in molecule.smi (1848/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1957210 in molecule.smi (1849/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1957211 in molecule.smi (1850/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1957212 in molecule.smi (1851/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1957213 in molecule.smi (1852/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1957214 in molecule.smi (1853/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1957215 in molecule.smi (1854/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1957216 in molecule.smi (1855/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1957217 in molecule.smi (1856/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1957218 in molecule.smi (1857/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1957219 in molecule.smi (1858/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL1957222 in molecule.smi (1861/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1957221 in molecule.smi (1860/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1957223 in molecule.smi (1862/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL165 in molecule.smi (1863/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1444 in molecule.smi (1864/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL9298 in molecule.smi (1866/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1399 in molecule.smi (1865/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1399 in molecule.smi (1867/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL117 in molecule.smi (1868/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL131921 in molecule.smi (1869/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2011191 in molecule.smi (1870/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2011192 in molecule.smi (1871/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2011193 in molecule.smi (1872/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL274826 in molecule.smi (1873/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2011171 in molecule.smi (1874/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2011172 in molecule.smi (1875/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL2011174 in molecule.smi (1877/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2011175 in molecule.smi (1878/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2011176 in molecule.smi (1879/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2011177 in molecule.smi (1880/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2011180 in molecule.smi (1881/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1165711 in molecule.smi (1882/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2011181 in molecule.smi (1883/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2011182 in molecule.smi (1884/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2011183 in molecule.smi (1885/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2011184 in molecule.smi (1886/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2011185 in molecule.smi (1887/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2011186 in molecule.smi (1888/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2011187 in molecule.smi (1889/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2011188 in molecule.smi (1890/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2011189 in molecule.smi (1891/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2011190 in molecule.smi (1892/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL157101 in molecule.smi (1893/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1077603 in molecule.smi (1894/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1629804 in molecule.smi (1895/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2058266 in molecule.smi (1896/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1629800 in molecule.smi (1897/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1629802 in molecule.smi (1898/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2058267 in molecule.smi (1899/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2058268 in molecule.smi (1900/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL132530 in molecule.smi (1901/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2163638 in molecule.smi (1902/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2163639 in molecule.smi (1903/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2163646 in molecule.smi (1904/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL9298 in molecule.smi (1905/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2163641 in molecule.smi (1906/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL9298 in molecule.smi (1907/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2165325 in molecule.smi (1908/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2165324 in molecule.smi (1909/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2165323 in molecule.smi (1910/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2165322 in molecule.smi (1911/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2165321 in molecule.smi (1912/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2165320 in molecule.smi (1913/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2165319 in molecule.smi (1914/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2165318 in molecule.smi (1915/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2165317 in molecule.smi (1916/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2165316 in molecule.smi (1917/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2165315 in molecule.smi (1918/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2165314 in molecule.smi (1919/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL446083 in molecule.smi (1920/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2165313 in molecule.smi (1921/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL62811 in molecule.smi (1922/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1083653 in molecule.smi (1923/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1444 in molecule.smi (1924/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL132530 in molecule.smi (1925/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1200374 in molecule.smi (1926/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2179114 in molecule.smi (1927/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2179113 in molecule.smi (1928/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2179112 in molecule.smi (1929/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2179111 in molecule.smi (1930/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2179109 in molecule.smi (1932/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2179110 in molecule.smi (1931/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2179108 in molecule.smi (1933/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2179107 in molecule.smi (1934/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL9352 in molecule.smi (1935/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2208141 in molecule.smi (1936/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2208140 in molecule.smi (1937/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2208131 in molecule.smi (1938/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1939530 in molecule.smi (1939/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL264401 in molecule.smi (1940/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL165 in molecule.smi (1941/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL488 in molecule.smi (1942/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2235953 in molecule.smi (1943/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2235952 in molecule.smi (1944/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2235951 in molecule.smi (1945/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2235950 in molecule.smi (1946/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL488 in molecule.smi (1947/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2282047 in molecule.smi (1948/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2282050 in molecule.smi (1949/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2282053 in molecule.smi (1950/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2312963 in molecule.smi (1951/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2312962 in molecule.smi (1952/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2312961 in molecule.smi (1953/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2312960 in molecule.smi (1954/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2312959 in molecule.smi (1955/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2312958 in molecule.smi (1956/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2312957 in molecule.smi (1957/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2312957 in molecule.smi (1958/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2312957 in molecule.smi (1959/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2312956 in molecule.smi (1960/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2312955 in molecule.smi (1961/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2312954 in molecule.smi (1962/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2312953 in molecule.smi (1963/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2312952 in molecule.smi (1964/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2312951 in molecule.smi (1965/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2312950 in molecule.smi (1966/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2312949 in molecule.smi (1967/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2311553 in molecule.smi (1968/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2312948 in molecule.smi (1969/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1444 in molecule.smi (1970/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL9298 in molecule.smi (1971/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2312972 in molecule.smi (1973/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2163639 in molecule.smi (1972/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2312971 in molecule.smi (1974/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2312970 in molecule.smi (1975/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2312969 in molecule.smi (1976/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2312968 in molecule.smi (1977/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2312967 in molecule.smi (1978/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2312966 in molecule.smi (1979/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2312965 in molecule.smi (1980/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2312964 in molecule.smi (1981/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL254328 in molecule.smi (1982/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL9298 in molecule.smi (1983/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2333337 in molecule.smi (1984/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2333336 in molecule.smi (1985/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2333335 in molecule.smi (1986/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2333334 in molecule.smi (1987/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2333333 in molecule.smi (1988/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2333332 in molecule.smi (1989/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2333331 in molecule.smi (1990/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2333330 in molecule.smi (1991/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2333329 in molecule.smi (1992/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2333328 in molecule.smi (1993/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2333327 in molecule.smi (1994/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2333326 in molecule.smi (1995/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL442373 in molecule.smi (1996/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL78322 in molecule.smi (1997/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL307160 in molecule.smi (1998/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1444 in molecule.smi (1999/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2336855 in molecule.smi (2000/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2336854 in molecule.smi (2001/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2336853 in molecule.smi (2002/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2336852 in molecule.smi (2003/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2336851 in molecule.smi (2004/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2336850 in molecule.smi (2005/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2336849 in molecule.smi (2006/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2336848 in molecule.smi (2007/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2336865 in molecule.smi (2009/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2336847 in molecule.smi (2008/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2336864 in molecule.smi (2010/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2336863 in molecule.smi (2011/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2336862 in molecule.smi (2012/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2336861 in molecule.smi (2013/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2336860 in molecule.smi (2014/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2336859 in molecule.smi (2015/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2336858 in molecule.smi (2016/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2336857 in molecule.smi (2017/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2336856 in molecule.smi (2018/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2137046 in molecule.smi (2019/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1093458 in molecule.smi (2020/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2386286 in molecule.smi (2021/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL149791 in molecule.smi (2022/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2386284 in molecule.smi (2023/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3350384 in molecule.smi (2025/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2386285 in molecule.smi (2024/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2386285 in molecule.smi (2026/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3350384 in molecule.smi (2027/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2420688 in molecule.smi (2028/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2420687 in molecule.smi (2029/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2420686 in molecule.smi (2030/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2420685 in molecule.smi (2031/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2420684 in molecule.smi (2032/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2420683 in molecule.smi (2033/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2420682 in molecule.smi (2034/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2420681 in molecule.smi (2035/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2420680 in molecule.smi (2036/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2420679 in molecule.smi (2037/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2420678 in molecule.smi (2038/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2420677 in molecule.smi (2039/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2420676 in molecule.smi (2040/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2420675 in molecule.smi (2041/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2420674 in molecule.smi (2042/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL371430 in molecule.smi (2043/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL193863 in molecule.smi (2044/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL365735 in molecule.smi (2045/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL9352 in molecule.smi (2046/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2430197 in molecule.smi (2047/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2430196 in molecule.smi (2048/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL9352 in molecule.smi (2049/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2431527 in molecule.smi (2050/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2431526 in molecule.smi (2051/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2431525 in molecule.smi (2052/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2431524 in molecule.smi (2053/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL132530 in molecule.smi (2054/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1444 in molecule.smi (2055/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2442766 in molecule.smi (2056/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2442765 in molecule.smi (2057/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2442764 in molecule.smi (2058/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2442763 in molecule.smi (2059/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2442762 in molecule.smi (2060/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2442761 in molecule.smi (2061/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2442760 in molecule.smi (2062/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2442759 in molecule.smi (2063/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2442758 in molecule.smi (2064/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2442757 in molecule.smi (2065/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2442756 in molecule.smi (2066/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2442755 in molecule.smi (2067/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2442754 in molecule.smi (2068/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2442753 in molecule.smi (2069/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2440145 in molecule.smi (2070/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2442772 in molecule.smi (2071/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2442771 in molecule.smi (2072/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2442770 in molecule.smi (2073/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2442769 in molecule.smi (2074/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2442768 in molecule.smi (2075/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2442767 in molecule.smi (2076/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3099735 in molecule.smi (2077/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1095413 in molecule.smi (2078/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3099734 in molecule.smi (2079/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3099686 in molecule.smi (2081/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3099695 in molecule.smi (2080/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3099683 in molecule.smi (2082/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL287677 in molecule.smi (2083/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2011186 in molecule.smi (2084/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2011177 in molecule.smi (2085/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL274318 in molecule.smi (2086/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL275638 in molecule.smi (2087/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1444 in molecule.smi (2088/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3105528 in molecule.smi (2089/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3105527 in molecule.smi (2090/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3105526 in molecule.smi (2091/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3105524 in molecule.smi (2092/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3105522 in molecule.smi (2093/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3105521 in molecule.smi (2094/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3105520 in molecule.smi (2095/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3105519 in molecule.smi (2096/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3105517 in molecule.smi (2097/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3134107 in molecule.smi (2099/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3134108 in molecule.smi (2098/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL399910 in molecule.smi (2100/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL117 in molecule.smi (2101/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL53343 in molecule.smi (2102/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3134111 in molecule.smi (2103/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3134115 in molecule.smi (2104/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3134110 in molecule.smi (2105/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3134114 in molecule.smi (2106/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3134112 in molecule.smi (2107/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3134109 in molecule.smi (2108/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3134113 in molecule.smi (2109/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1444 in molecule.smi (2110/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3134228 in molecule.smi (2111/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3221175 in molecule.smi (2112/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3219070 in molecule.smi (2113/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1444 in molecule.smi (2114/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL159073 in molecule.smi (2115/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1083665 in molecule.smi (2116/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3219600 in molecule.smi (2117/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL451168 in molecule.smi (2118/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL132530 in molecule.smi (2119/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL934 in molecule.smi (2120/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3359965 in molecule.smi (2121/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3359964 in molecule.smi (2122/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3359963 in molecule.smi (2123/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3359962 in molecule.smi (2124/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3359961 in molecule.smi (2125/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3359960 in molecule.smi (2126/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3359959 in molecule.smi (2127/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3359958 in molecule.smi (2128/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1688901 in molecule.smi (2130/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3359957 in molecule.smi (2129/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3359956 in molecule.smi (2132/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1688900 in molecule.smi (2131/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3359955 in molecule.smi (2133/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3359954 in molecule.smi (2134/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1688898 in molecule.smi (2135/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3359953 in molecule.smi (2136/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1688899 in molecule.smi (2137/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1688897 in molecule.smi (2138/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3359952 in molecule.smi (2139/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1688895 in molecule.smi (2140/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1688894 in molecule.smi (2141/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1688892 in molecule.smi (2142/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1688890 in molecule.smi (2143/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1688891 in molecule.smi (2144/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1688893 in molecule.smi (2145/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL225499 in molecule.smi (2146/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL437731 in molecule.smi (2147/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3099695 in molecule.smi (2148/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL9298 in molecule.smi (2149/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3315157 in molecule.smi (2150/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL9352 in molecule.smi (2151/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL465881 in molecule.smi (2152/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL488 in molecule.smi (2153/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL9298 in molecule.smi (2154/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1200374 in molecule.smi (2155/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3407539 in molecule.smi (2156/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3407538 in molecule.smi (2157/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3407537 in molecule.smi (2158/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3407536 in molecule.smi (2159/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1200374 in molecule.smi (2160/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3407539 in molecule.smi (2161/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3407538 in molecule.smi (2162/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3407537 in molecule.smi (2163/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3407536 in molecule.smi (2164/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3415152 in molecule.smi (2165/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3415153 in molecule.smi (2166/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3415154 in molecule.smi (2167/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3415155 in molecule.smi (2168/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3415156 in molecule.smi (2169/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3415157 in molecule.smi (2170/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3415158 in molecule.smi (2171/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3415159 in molecule.smi (2172/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3415160 in molecule.smi (2173/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3415161 in molecule.smi (2174/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3415162 in molecule.smi (2175/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3415163 in molecule.smi (2176/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3415164 in molecule.smi (2177/2806). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3415165 in molecule.smi (2178/2806). Average speed: 0.07 s/mol.\n",
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      "Processing CHEMBL3751984 in molecule.smi (2389/2806). Average speed: 0.08 s/mol.\n",
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      "Processing CHEMBL2003274 in molecule.smi (2390/2806). Average speed: 0.08 s/mol.\n",
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      "Processing CHEMBL3793091 in molecule.smi (2394/2806). Average speed: 0.08 s/mol.\n",
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      "Processing CHEMBL3806151 in molecule.smi (2409/2806). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL3805851 in molecule.smi (2410/2806). Average speed: 0.08 s/mol.\n",
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      "Descriptor calculation completed in 3 mins 36.806 secs . Average speed: 0.08 s/mol.\n"
     ]
    }
   ],
   "source": [
    "!bash padel.sh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_prep= pd.read_csv(\"descriptors_output.csv\")\n",
    "df_prep_X = df_prep.copy()\n",
    "df_prep_X = df_prep_X.drop(columns=[\"Name\"])\n",
    "df_prep_y = df_pIC50[\"pIC50\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.concat([df_prep_X,df_prep_y],axis=1)\n",
    "dataset.to_csv(\"train_and_test.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Building and Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing libraries\n",
    "from sklearn.model_selection import train_test_split,GridSearchCV\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.feature_selection import VarianceThreshold\n",
    "from sklearn.metrics import mean_squared_error,mean_absolute_error,r2_score\n",
    "from sklearn.preprocessing import MinMaxScaler,StandardScaler\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def metrics(X_train,X_test,y_train,y_test,model):\n",
    "    y_pred = model.predict(X_test)\n",
    "    print(\"Training set accuracy:{:0.2f} \".format(model.score(X_train,y_train)))\n",
    "    print(\"Test set accuracy:{:0.2f} \".format(model.score(X_test,y_test)))\n",
    "    print(\"Mean square error:{:0.2f} \".format(mean_squared_error(y_test,y_pred)))\n",
    "    print(\"Mean absolute error:{:0.2f} \".format(mean_absolute_error(y_test,y_pred)))\n",
    "    print(\"Root Mean square error:{:0.2f} \".format((mean_squared_error(y_test,y_pred))**0.5))\n",
    "    print(\"-------------------------------------\")\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"train_and_test.csv\")\n",
    "X = df.drop(\"pIC50\",axis=1)\n",
    "y = df.pIC50\n",
    "\n",
    "#Removing low variance features\n",
    "selection = VarianceThreshold(threshold = 0.8 * (1 - 0.8))\n",
    "X = selection.fit_transform(X)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,random_state=0)\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set accuracy:0.23 \n",
      "Test set accuracy:0.21 \n",
      "Mean square error:1.44 \n",
      "Mean absolute error:0.93 \n",
      "Root Mean square error:1.20 \n",
      "-------------------------------------\n",
      "Training set accuracy:0.28 \n",
      "Test set accuracy:0.26 \n",
      "Mean square error:1.35 \n",
      "Mean absolute error:0.89 \n",
      "Root Mean square error:1.16 \n",
      "-------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#unoptimised models\n",
    "ran_for = RandomForestRegressor(n_estimators=100, max_depth=3)\n",
    "ran_for.fit(X_train_scaled, y_train)\n",
    "metrics(X_train_scaled,X_test_scaled,y_train,y_test,ran_for)\n",
    "\n",
    "#optimised model\n",
    "ran_for = RandomForestRegressor(n_estimators=100, max_depth=5,max_features='sqrt')\n",
    "ran_for.fit(X_train_scaled, y_train)\n",
    "metrics(X_train_scaled,X_test_scaled,y_train,y_test,ran_for)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = {\n",
    "    'max_depth': [5, 20, 60, 90],\n",
    "    'max_features': ['auto', 'sqrt'],\n",
    "    'n_estimators': [100, 300, 500]\n",
    "}\n",
    "rf_grid = GridSearchCV(estimator=ran_for,param_grid=grid,cv=3,scoring='neg_mean_squared_error').fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "ran_for_grid = pd.DataFrame(rf_grid.cv_results_)\n",
    "ran_for_grid.to_csv('grid_ran_for.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set accuracy:0.37 \n",
      "Test set accuracy:0.28 \n",
      "Mean square error:1.32 \n",
      "Mean absolute error:0.86 \n",
      "Root Mean square error:1.15 \n",
      "-------------------------------------\n",
      "Training set accuracy:0.44 \n",
      "Test set accuracy:0.26 \n",
      "Mean square error:1.36 \n",
      "Mean absolute error:0.87 \n",
      "Root Mean square error:1.16 \n",
      "-------------------------------------\n"
     ]
    }
   ],
   "source": [
    "svr = SVR(kernel='poly').fit(X_train_scaled, y_train)\n",
    "metrics(X_train_scaled,X_test_scaled,y_train,y_test,svr)\n",
    "\n",
    "svr = SVR(kernel='poly',C=1, gamma=0.01).fit(X_train_scaled, y_train)\n",
    "metrics(X_train_scaled,X_test_scaled,y_train,y_test,svr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "C_range = 10. ** np.arange(-3, 8)\n",
    "gamma_range = 10. ** np.arange(-5, 4)\n",
    "grid_2 = dict(gamma=gamma_range, C=C_range)\n",
    "svr_grid = GridSearchCV(estimator=svr,param_grid=grid_2,cv=3).fit(X_train_scaled,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "svr_grid = pd.DataFrame(svm_grid.cv_results_)\n",
    "svr_grid.to_csv('grid_svm.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "60/60 [==============================] - 2s 19ms/step - loss: 3.1868 - accuracy: 0.0032 - val_loss: 1.3996 - val_accuracy: 0.0000e+00\n",
      "Epoch 2/200\n",
      "60/60 [==============================] - 1s 12ms/step - loss: 1.7097 - accuracy: 0.0032 - val_loss: 1.5381 - val_accuracy: 0.0000e+00\n",
      "Epoch 3/200\n",
      "60/60 [==============================] - 1s 12ms/step - loss: 1.5880 - accuracy: 0.0032 - val_loss: 1.3576 - val_accuracy: 0.0000e+00\n",
      "Epoch 4/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.5537 - accuracy: 0.0032 - val_loss: 1.4297 - val_accuracy: 0.0000e+00\n",
      "Epoch 5/200\n",
      "60/60 [==============================] - 1s 12ms/step - loss: 1.5856 - accuracy: 0.0032 - val_loss: 1.4533 - val_accuracy: 0.0000e+00\n",
      "Epoch 6/200\n",
      "60/60 [==============================] - 1s 12ms/step - loss: 1.5605 - accuracy: 0.0032 - val_loss: 1.4431 - val_accuracy: 0.0000e+00\n",
      "Epoch 7/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.4565 - accuracy: 0.0032 - val_loss: 1.5868 - val_accuracy: 0.0000e+00\n",
      "Epoch 8/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.4507 - accuracy: 0.0032 - val_loss: 1.5768 - val_accuracy: 0.0000e+00\n",
      "Epoch 9/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.4444 - accuracy: 0.0032 - val_loss: 2.3118 - val_accuracy: 0.0000e+00\n",
      "Epoch 10/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.3896 - accuracy: 0.0032 - val_loss: 1.4660 - val_accuracy: 0.0000e+00\n",
      "Epoch 11/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.3743 - accuracy: 0.0032 - val_loss: 1.8639 - val_accuracy: 0.0000e+00\n",
      "Epoch 12/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.4266 - accuracy: 0.0032 - val_loss: 1.5494 - val_accuracy: 0.0000e+00\n",
      "Epoch 13/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.4168 - accuracy: 0.0032 - val_loss: 1.5164 - val_accuracy: 0.0000e+00\n",
      "Epoch 14/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.3203 - accuracy: 0.0032 - val_loss: 1.5702 - val_accuracy: 0.0000e+00\n",
      "Epoch 15/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.3180 - accuracy: 0.0032 - val_loss: 1.7390 - val_accuracy: 0.0000e+00\n",
      "Epoch 16/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.2988 - accuracy: 0.0032 - val_loss: 1.4257 - val_accuracy: 0.0000e+00\n",
      "Epoch 17/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.2658 - accuracy: 0.0032 - val_loss: 1.5190 - val_accuracy: 0.0000e+00\n",
      "Epoch 18/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.2970 - accuracy: 0.0032 - val_loss: 1.4515 - val_accuracy: 0.0000e+00\n",
      "Epoch 19/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.2646 - accuracy: 0.0032 - val_loss: 2.0810 - val_accuracy: 0.0000e+00\n",
      "Epoch 20/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.2757 - accuracy: 0.0032 - val_loss: 1.7365 - val_accuracy: 0.0000e+00\n",
      "Epoch 21/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.2787 - accuracy: 0.0032 - val_loss: 1.5141 - val_accuracy: 0.0000e+00\n",
      "Epoch 22/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.3061 - accuracy: 0.0032 - val_loss: 1.4471 - val_accuracy: 0.0000e+00\n",
      "Epoch 23/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.1896 - accuracy: 0.0032 - val_loss: 1.6569 - val_accuracy: 0.0000e+00\n",
      "Epoch 24/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.2122 - accuracy: 0.0032 - val_loss: 1.4546 - val_accuracy: 0.0000e+00\n",
      "Epoch 25/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.1686 - accuracy: 0.0032 - val_loss: 1.6284 - val_accuracy: 0.0000e+00\n",
      "Epoch 26/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.1784 - accuracy: 0.0032 - val_loss: 1.6867 - val_accuracy: 0.0000e+00\n",
      "Epoch 27/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.2269 - accuracy: 0.0032 - val_loss: 1.6460 - val_accuracy: 0.0000e+00\n",
      "Epoch 28/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.2850 - accuracy: 0.0032 - val_loss: 1.7217 - val_accuracy: 0.0000e+00\n",
      "Epoch 29/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.1546 - accuracy: 0.0032 - val_loss: 1.7315 - val_accuracy: 0.0000e+00\n",
      "Epoch 30/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.2115 - accuracy: 0.0032 - val_loss: 1.6599 - val_accuracy: 0.0000e+00\n",
      "Epoch 31/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.1221 - accuracy: 0.0032 - val_loss: 2.1390 - val_accuracy: 0.0000e+00\n",
      "Epoch 32/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.1840 - accuracy: 0.0032 - val_loss: 1.5937 - val_accuracy: 0.0000e+00\n",
      "Epoch 33/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.2312 - accuracy: 0.0032 - val_loss: 1.6194 - val_accuracy: 0.0000e+00\n",
      "Epoch 34/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.2195 - accuracy: 0.0032 - val_loss: 1.6141 - val_accuracy: 0.0000e+00\n",
      "Epoch 35/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.1746 - accuracy: 0.0032 - val_loss: 1.4746 - val_accuracy: 0.0000e+00\n",
      "Epoch 36/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.1538 - accuracy: 0.0032 - val_loss: 1.6175 - val_accuracy: 0.0000e+00\n",
      "Epoch 37/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.1490 - accuracy: 0.0032 - val_loss: 1.6002 - val_accuracy: 0.0000e+00\n",
      "Epoch 38/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.1000 - accuracy: 0.0032 - val_loss: 1.4425 - val_accuracy: 0.0000e+00\n",
      "Epoch 39/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.0919 - accuracy: 0.0032 - val_loss: 1.5903 - val_accuracy: 0.0000e+00\n",
      "Epoch 40/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.1081 - accuracy: 0.0032 - val_loss: 1.6729 - val_accuracy: 0.0000e+00\n",
      "Epoch 41/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.1277 - accuracy: 0.0032 - val_loss: 1.6262 - val_accuracy: 0.0000e+00\n",
      "Epoch 42/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.1477 - accuracy: 0.0032 - val_loss: 1.7517 - val_accuracy: 0.0000e+00\n",
      "Epoch 43/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.1181 - accuracy: 0.0032 - val_loss: 1.6908 - val_accuracy: 0.0000e+00\n",
      "Epoch 44/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.1571 - accuracy: 0.0032 - val_loss: 1.6608 - val_accuracy: 0.0000e+00\n",
      "Epoch 45/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.1592 - accuracy: 0.0032 - val_loss: 1.8513 - val_accuracy: 0.0000e+00\n",
      "Epoch 46/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.1118 - accuracy: 0.0032 - val_loss: 1.6061 - val_accuracy: 0.0000e+00\n",
      "Epoch 47/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.0056 - accuracy: 0.0032 - val_loss: 1.7410 - val_accuracy: 0.0000e+00\n",
      "Epoch 48/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.0153 - accuracy: 0.0032 - val_loss: 1.7161 - val_accuracy: 0.0000e+00\n",
      "Epoch 49/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.1320 - accuracy: 0.0032 - val_loss: 1.7917 - val_accuracy: 0.0000e+00\n",
      "Epoch 50/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.0963 - accuracy: 0.0032 - val_loss: 1.9459 - val_accuracy: 0.0000e+00\n",
      "Epoch 51/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.0943 - accuracy: 0.0032 - val_loss: 1.6854 - val_accuracy: 0.0000e+00\n",
      "Epoch 52/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.0391 - accuracy: 0.0032 - val_loss: 1.6002 - val_accuracy: 0.0000e+00\n",
      "Epoch 53/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.1151 - accuracy: 0.0026 - val_loss: 1.7294 - val_accuracy: 0.0000e+00\n",
      "Epoch 54/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.1342 - accuracy: 0.0032 - val_loss: 1.6369 - val_accuracy: 0.0000e+00\n",
      "Epoch 55/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.0225 - accuracy: 0.0032 - val_loss: 1.5793 - val_accuracy: 0.0000e+00\n",
      "Epoch 56/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.0829 - accuracy: 0.0032 - val_loss: 1.8870 - val_accuracy: 0.0000e+00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 57/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.0490 - accuracy: 0.0032 - val_loss: 1.8523 - val_accuracy: 0.0000e+00\n",
      "Epoch 58/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.0727 - accuracy: 0.0032 - val_loss: 1.8953 - val_accuracy: 0.0000e+00\n",
      "Epoch 59/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.0511 - accuracy: 0.0032 - val_loss: 1.5792 - val_accuracy: 0.0000e+00\n",
      "Epoch 60/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.0829 - accuracy: 0.0032 - val_loss: 1.7537 - val_accuracy: 0.0000e+00\n",
      "Epoch 61/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.0395 - accuracy: 0.0032 - val_loss: 1.6936 - val_accuracy: 0.0000e+00\n",
      "Epoch 62/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.0358 - accuracy: 0.0032 - val_loss: 1.7989 - val_accuracy: 0.0000e+00\n",
      "Epoch 63/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.0619 - accuracy: 0.0032 - val_loss: 1.9191 - val_accuracy: 0.0000e+00\n",
      "Epoch 64/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.9760 - accuracy: 0.0032 - val_loss: 1.9168 - val_accuracy: 0.0000e+00\n",
      "Epoch 65/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.9786 - accuracy: 0.0032 - val_loss: 1.5691 - val_accuracy: 0.0000e+00\n",
      "Epoch 66/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.0925 - accuracy: 0.0026 - val_loss: 1.5874 - val_accuracy: 0.0000e+00\n",
      "Epoch 67/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.9569 - accuracy: 0.0032 - val_loss: 1.6637 - val_accuracy: 0.0000e+00\n",
      "Epoch 68/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.1399 - accuracy: 0.0032 - val_loss: 1.6737 - val_accuracy: 0.0000e+00\n",
      "Epoch 69/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.0440 - accuracy: 0.0032 - val_loss: 1.6398 - val_accuracy: 0.0000e+00\n",
      "Epoch 70/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.0945 - accuracy: 0.0032 - val_loss: 1.6687 - val_accuracy: 0.0000e+00\n",
      "Epoch 71/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.0441 - accuracy: 0.0032 - val_loss: 1.7916 - val_accuracy: 0.0000e+00\n",
      "Epoch 72/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.9976 - accuracy: 0.0032 - val_loss: 1.7337 - val_accuracy: 0.0000e+00\n",
      "Epoch 73/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.0449 - accuracy: 0.0032 - val_loss: 1.5546 - val_accuracy: 0.0000e+00\n",
      "Epoch 74/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.0190 - accuracy: 0.0032 - val_loss: 1.8401 - val_accuracy: 0.0000e+00\n",
      "Epoch 75/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.0062 - accuracy: 0.0032 - val_loss: 1.6550 - val_accuracy: 0.0000e+00\n",
      "Epoch 76/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.0357 - accuracy: 0.0032 - val_loss: 1.7548 - val_accuracy: 0.0000e+00\n",
      "Epoch 77/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.9595 - accuracy: 0.0032 - val_loss: 1.6612 - val_accuracy: 0.0000e+00\n",
      "Epoch 78/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.9335 - accuracy: 0.0032 - val_loss: 1.6276 - val_accuracy: 0.0000e+00\n",
      "Epoch 79/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.9721 - accuracy: 0.0032 - val_loss: 1.6369 - val_accuracy: 0.0000e+00\n",
      "Epoch 80/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.9072 - accuracy: 0.0032 - val_loss: 1.6675 - val_accuracy: 0.0000e+00\n",
      "Epoch 81/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.9780 - accuracy: 0.0032 - val_loss: 1.5665 - val_accuracy: 0.0000e+00\n",
      "Epoch 82/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.0185 - accuracy: 0.0026 - val_loss: 1.7887 - val_accuracy: 0.0000e+00\n",
      "Epoch 83/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.9798 - accuracy: 0.0032 - val_loss: 1.5597 - val_accuracy: 0.0000e+00\n",
      "Epoch 84/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.9212 - accuracy: 0.0032 - val_loss: 1.5908 - val_accuracy: 0.0000e+00\n",
      "Epoch 85/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.0416 - accuracy: 0.0032 - val_loss: 1.8668 - val_accuracy: 0.0000e+00\n",
      "Epoch 86/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.9793 - accuracy: 0.0032 - val_loss: 1.6829 - val_accuracy: 0.0000e+00\n",
      "Epoch 87/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.0172 - accuracy: 0.0032 - val_loss: 1.6780 - val_accuracy: 0.0000e+00\n",
      "Epoch 88/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.9513 - accuracy: 0.0032 - val_loss: 1.9276 - val_accuracy: 0.0000e+00\n",
      "Epoch 89/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.0139 - accuracy: 0.0032 - val_loss: 1.5069 - val_accuracy: 0.0000e+00\n",
      "Epoch 90/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.0340 - accuracy: 0.0032 - val_loss: 1.6794 - val_accuracy: 0.0000e+00\n",
      "Epoch 91/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.0159 - accuracy: 0.0032 - val_loss: 1.5631 - val_accuracy: 0.0000e+00\n",
      "Epoch 92/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.9751 - accuracy: 0.0032 - val_loss: 1.5662 - val_accuracy: 0.0000e+00\n",
      "Epoch 93/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.9987 - accuracy: 0.0032 - val_loss: 1.4996 - val_accuracy: 0.0000e+00\n",
      "Epoch 94/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.9395 - accuracy: 0.0032 - val_loss: 1.6164 - val_accuracy: 0.0000e+00\n",
      "Epoch 95/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.9108 - accuracy: 0.0032 - val_loss: 1.5266 - val_accuracy: 0.0000e+00\n",
      "Epoch 96/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.0022 - accuracy: 0.0032 - val_loss: 1.5437 - val_accuracy: 0.0000e+00\n",
      "Epoch 97/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.9525 - accuracy: 0.0032 - val_loss: 1.8137 - val_accuracy: 0.0000e+00\n",
      "Epoch 98/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.9614 - accuracy: 0.0026 - val_loss: 1.6727 - val_accuracy: 0.0000e+00\n",
      "Epoch 99/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.0064 - accuracy: 0.0032 - val_loss: 1.4980 - val_accuracy: 0.0000e+00\n",
      "Epoch 100/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.9211 - accuracy: 0.0026 - val_loss: 1.5365 - val_accuracy: 0.0000e+00\n",
      "Epoch 101/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.9177 - accuracy: 0.0032 - val_loss: 1.4947 - val_accuracy: 0.0000e+00\n",
      "Epoch 102/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.9942 - accuracy: 0.0032 - val_loss: 1.6019 - val_accuracy: 0.0000e+00\n",
      "Epoch 103/200\n",
      "60/60 [==============================] - 1s 12ms/step - loss: 0.9370 - accuracy: 0.0032 - val_loss: 1.5494 - val_accuracy: 0.0000e+00\n",
      "Epoch 104/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.8843 - accuracy: 0.0032 - val_loss: 1.4799 - val_accuracy: 0.0000e+00\n",
      "Epoch 105/200\n",
      "60/60 [==============================] - 1s 12ms/step - loss: 0.8922 - accuracy: 0.0032 - val_loss: 1.6137 - val_accuracy: 0.0000e+00\n",
      "Epoch 106/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.9029 - accuracy: 0.0032 - val_loss: 1.5776 - val_accuracy: 0.0000e+00\n",
      "Epoch 107/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.0031 - accuracy: 0.0026 - val_loss: 1.4896 - val_accuracy: 0.0000e+00\n",
      "Epoch 108/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.9085 - accuracy: 0.0032 - val_loss: 1.5940 - val_accuracy: 0.0000e+00\n",
      "Epoch 109/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.9727 - accuracy: 0.0032 - val_loss: 1.5995 - val_accuracy: 0.0000e+00\n",
      "Epoch 110/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.9669 - accuracy: 0.0032 - val_loss: 1.8062 - val_accuracy: 0.0000e+00\n",
      "Epoch 111/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.9065 - accuracy: 0.0032 - val_loss: 1.6954 - val_accuracy: 0.0000e+00\n",
      "Epoch 112/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60/60 [==============================] - 1s 11ms/step - loss: 0.9204 - accuracy: 0.0032 - val_loss: 1.6365 - val_accuracy: 0.0000e+00\n",
      "Epoch 113/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.9182 - accuracy: 0.0032 - val_loss: 1.6357 - val_accuracy: 0.0000e+00\n",
      "Epoch 114/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.9937 - accuracy: 0.0032 - val_loss: 1.5587 - val_accuracy: 0.0000e+00\n",
      "Epoch 115/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.9001 - accuracy: 0.0032 - val_loss: 1.6140 - val_accuracy: 0.0000e+00\n",
      "Epoch 116/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.9301 - accuracy: 0.0032 - val_loss: 1.5617 - val_accuracy: 0.0000e+00\n",
      "Epoch 117/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.8612 - accuracy: 0.0032 - val_loss: 1.5795 - val_accuracy: 0.0000e+00\n",
      "Epoch 118/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.9124 - accuracy: 0.0032 - val_loss: 1.6102 - val_accuracy: 0.0000e+00\n",
      "Epoch 119/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8744 - accuracy: 0.0032 - val_loss: 1.6569 - val_accuracy: 0.0000e+00\n",
      "Epoch 120/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8849 - accuracy: 0.0032 - val_loss: 1.6701 - val_accuracy: 0.0000e+00\n",
      "Epoch 121/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8859 - accuracy: 0.0032 - val_loss: 1.7709 - val_accuracy: 0.0000e+00\n",
      "Epoch 122/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.8817 - accuracy: 0.0032 - val_loss: 1.5530 - val_accuracy: 0.0000e+00\n",
      "Epoch 123/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.8773 - accuracy: 0.0032 - val_loss: 1.5648 - val_accuracy: 0.0000e+00\n",
      "Epoch 124/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8897 - accuracy: 0.0032 - val_loss: 1.5644 - val_accuracy: 0.0000e+00\n",
      "Epoch 125/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.8967 - accuracy: 0.0032 - val_loss: 1.5732 - val_accuracy: 0.0000e+00\n",
      "Epoch 126/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.9254 - accuracy: 0.0026 - val_loss: 1.6309 - val_accuracy: 0.0000e+00\n",
      "Epoch 127/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 1.1458 - accuracy: 0.0032 - val_loss: 1.6834 - val_accuracy: 0.0000e+00\n",
      "Epoch 128/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 1.0377 - accuracy: 0.0032 - val_loss: 1.5871 - val_accuracy: 0.0000e+00\n",
      "Epoch 129/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.9773 - accuracy: 0.0032 - val_loss: 1.7084 - val_accuracy: 0.0000e+00\n",
      "Epoch 130/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.9586 - accuracy: 0.0032 - val_loss: 1.6239 - val_accuracy: 0.0000e+00\n",
      "Epoch 131/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.9657 - accuracy: 0.0032 - val_loss: 1.6397 - val_accuracy: 0.0000e+00\n",
      "Epoch 132/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.9813 - accuracy: 0.0026 - val_loss: 1.5544 - val_accuracy: 0.0000e+00\n",
      "Epoch 133/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.9658 - accuracy: 0.0032 - val_loss: 1.7610 - val_accuracy: 0.0000e+00\n",
      "Epoch 134/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.9340 - accuracy: 0.0032 - val_loss: 1.6425 - val_accuracy: 0.0000e+00\n",
      "Epoch 135/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8543 - accuracy: 0.0032 - val_loss: 1.5935 - val_accuracy: 0.0000e+00\n",
      "Epoch 136/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8521 - accuracy: 0.0032 - val_loss: 1.5149 - val_accuracy: 0.0000e+00\n",
      "Epoch 137/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8472 - accuracy: 0.0032 - val_loss: 1.5959 - val_accuracy: 0.0000e+00\n",
      "Epoch 138/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8775 - accuracy: 0.0032 - val_loss: 1.6300 - val_accuracy: 0.0000e+00\n",
      "Epoch 139/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8616 - accuracy: 0.0032 - val_loss: 1.6682 - val_accuracy: 0.0000e+00\n",
      "Epoch 140/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8507 - accuracy: 0.0032 - val_loss: 1.7140 - val_accuracy: 0.0000e+00\n",
      "Epoch 141/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8580 - accuracy: 0.0032 - val_loss: 1.6780 - val_accuracy: 0.0000e+00\n",
      "Epoch 142/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8478 - accuracy: 0.0032 - val_loss: 1.6101 - val_accuracy: 0.0000e+00\n",
      "Epoch 143/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8499 - accuracy: 0.0032 - val_loss: 1.5421 - val_accuracy: 0.0000e+00\n",
      "Epoch 144/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.9096 - accuracy: 0.0032 - val_loss: 1.6907 - val_accuracy: 0.0000e+00\n",
      "Epoch 145/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.9510 - accuracy: 0.0032 - val_loss: 1.5950 - val_accuracy: 0.0000e+00\n",
      "Epoch 146/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.9436 - accuracy: 0.0032 - val_loss: 1.7366 - val_accuracy: 0.0000e+00\n",
      "Epoch 147/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.9857 - accuracy: 0.0032 - val_loss: 1.6797 - val_accuracy: 0.0000e+00\n",
      "Epoch 148/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8865 - accuracy: 0.0032 - val_loss: 1.6763 - val_accuracy: 0.0000e+00\n",
      "Epoch 149/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.8625 - accuracy: 0.0032 - val_loss: 1.6906 - val_accuracy: 0.0000e+00\n",
      "Epoch 150/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8495 - accuracy: 0.0032 - val_loss: 1.6831 - val_accuracy: 0.0000e+00\n",
      "Epoch 151/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8625 - accuracy: 0.0032 - val_loss: 1.6314 - val_accuracy: 0.0000e+00\n",
      "Epoch 152/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.8440 - accuracy: 0.0032 - val_loss: 1.6459 - val_accuracy: 0.0000e+00\n",
      "Epoch 153/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.8795 - accuracy: 0.0032 - val_loss: 1.9855 - val_accuracy: 0.0000e+00\n",
      "Epoch 154/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.9239 - accuracy: 0.0032 - val_loss: 1.5658 - val_accuracy: 0.0000e+00\n",
      "Epoch 155/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.9002 - accuracy: 0.0032 - val_loss: 1.6044 - val_accuracy: 0.0000e+00\n",
      "Epoch 156/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.9196 - accuracy: 0.0026 - val_loss: 1.6408 - val_accuracy: 0.0000e+00\n",
      "Epoch 157/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.8560 - accuracy: 0.0032 - val_loss: 1.6682 - val_accuracy: 0.0000e+00\n",
      "Epoch 158/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8558 - accuracy: 0.0026 - val_loss: 1.5605 - val_accuracy: 0.0000e+00\n",
      "Epoch 159/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.9387 - accuracy: 0.0032 - val_loss: 1.5765 - val_accuracy: 0.0000e+00\n",
      "Epoch 160/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.8698 - accuracy: 0.0032 - val_loss: 1.6109 - val_accuracy: 0.0000e+00\n",
      "Epoch 161/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.9264 - accuracy: 0.0032 - val_loss: 1.6120 - val_accuracy: 0.0000e+00\n",
      "Epoch 162/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.9384 - accuracy: 0.0032 - val_loss: 1.5636 - val_accuracy: 0.0000e+00\n",
      "Epoch 163/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8551 - accuracy: 0.0032 - val_loss: 1.5945 - val_accuracy: 0.0000e+00\n",
      "Epoch 164/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8395 - accuracy: 0.0032 - val_loss: 1.5710 - val_accuracy: 0.0000e+00\n",
      "Epoch 165/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8648 - accuracy: 0.0032 - val_loss: 1.6066 - val_accuracy: 0.0000e+00\n",
      "Epoch 166/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8437 - accuracy: 0.0032 - val_loss: 1.5814 - val_accuracy: 0.0000e+00\n",
      "Epoch 167/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8630 - accuracy: 0.0032 - val_loss: 1.6856 - val_accuracy: 0.0000e+00\n",
      "Epoch 168/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8727 - accuracy: 0.0032 - val_loss: 1.5805 - val_accuracy: 0.0000e+00\n",
      "Epoch 169/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8858 - accuracy: 0.0032 - val_loss: 1.5655 - val_accuracy: 0.0000e+00\n",
      "Epoch 170/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8879 - accuracy: 0.0032 - val_loss: 1.6299 - val_accuracy: 0.0000e+00\n",
      "Epoch 171/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.8780 - accuracy: 0.0032 - val_loss: 1.6195 - val_accuracy: 0.0000e+00\n",
      "Epoch 172/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.8562 - accuracy: 0.0032 - val_loss: 1.5675 - val_accuracy: 0.0000e+00\n",
      "Epoch 173/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8318 - accuracy: 0.0032 - val_loss: 1.5398 - val_accuracy: 0.0000e+00\n",
      "Epoch 174/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.8499 - accuracy: 0.0032 - val_loss: 1.6751 - val_accuracy: 0.0000e+00\n",
      "Epoch 175/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8487 - accuracy: 0.0032 - val_loss: 1.5994 - val_accuracy: 0.0000e+00\n",
      "Epoch 176/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8630 - accuracy: 0.0032 - val_loss: 1.6509 - val_accuracy: 0.0000e+00\n",
      "Epoch 177/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.8421 - accuracy: 0.0032 - val_loss: 1.6932 - val_accuracy: 0.0000e+00\n",
      "Epoch 178/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.8630 - accuracy: 0.0032 - val_loss: 1.6249 - val_accuracy: 0.0000e+00\n",
      "Epoch 179/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8394 - accuracy: 0.0032 - val_loss: 1.5320 - val_accuracy: 0.0000e+00\n",
      "Epoch 180/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.8356 - accuracy: 0.0032 - val_loss: 1.6075 - val_accuracy: 0.0000e+00\n",
      "Epoch 181/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8550 - accuracy: 0.0032 - val_loss: 1.6214 - val_accuracy: 0.0000e+00\n",
      "Epoch 182/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.8240 - accuracy: 0.0032 - val_loss: 1.6458 - val_accuracy: 0.0000e+00\n",
      "Epoch 183/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8710 - accuracy: 0.0032 - val_loss: 1.5526 - val_accuracy: 0.0000e+00\n",
      "Epoch 184/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8204 - accuracy: 0.0032 - val_loss: 1.5931 - val_accuracy: 0.0000e+00\n",
      "Epoch 185/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8125 - accuracy: 0.0032 - val_loss: 1.5799 - val_accuracy: 0.0000e+00\n",
      "Epoch 186/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8260 - accuracy: 0.0032 - val_loss: 1.6601 - val_accuracy: 0.0000e+00\n",
      "Epoch 187/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8426 - accuracy: 0.0032 - val_loss: 1.5879 - val_accuracy: 0.0000e+00\n",
      "Epoch 188/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8267 - accuracy: 0.0026 - val_loss: 1.5589 - val_accuracy: 0.0000e+00\n",
      "Epoch 189/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8487 - accuracy: 0.0032 - val_loss: 1.6157 - val_accuracy: 0.0000e+00\n",
      "Epoch 190/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8327 - accuracy: 0.0032 - val_loss: 1.5817 - val_accuracy: 0.0000e+00\n",
      "Epoch 191/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8470 - accuracy: 0.0032 - val_loss: 1.5532 - val_accuracy: 0.0000e+00\n",
      "Epoch 192/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8237 - accuracy: 0.0032 - val_loss: 1.5758 - val_accuracy: 0.0000e+00\n",
      "Epoch 193/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8407 - accuracy: 0.0026 - val_loss: 1.5470 - val_accuracy: 0.0000e+00\n",
      "Epoch 194/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.8074 - accuracy: 0.0032 - val_loss: 1.6025 - val_accuracy: 0.0000e+00\n",
      "Epoch 195/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.8217 - accuracy: 0.0032 - val_loss: 1.6208 - val_accuracy: 0.0000e+00\n",
      "Epoch 196/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.8255 - accuracy: 0.0032 - val_loss: 1.6159 - val_accuracy: 0.0000e+00\n",
      "Epoch 197/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.8172 - accuracy: 0.0032 - val_loss: 1.5501 - val_accuracy: 0.0000e+00\n",
      "Epoch 198/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.8071 - accuracy: 0.0032 - val_loss: 1.6057 - val_accuracy: 0.0000e+00\n",
      "Epoch 199/200\n",
      "60/60 [==============================] - 1s 11ms/step - loss: 0.8363 - accuracy: 0.0032 - val_loss: 1.5571 - val_accuracy: 0.0000e+00\n",
      "Epoch 200/200\n",
      "60/60 [==============================] - 1s 10ms/step - loss: 0.8469 - accuracy: 0.0032 - val_loss: 1.6276 - val_accuracy: 0.0000e+00\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x123c1d2d288>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.losses import MeanSquaredError\n",
    "from keras.optimizers import Adam\n",
    "\n",
    "model = Sequential([\n",
    "    Dense(600, activation='relu'),\n",
    "    Dense(600, activation='relu'),\n",
    "    Dense(600, activation='relu'),\n",
    "    Dense(1, activation='linear'),\n",
    "])\n",
    "opt = Adam(learning_rate=0.0005)\n",
    "mse = MeanSquaredError()\n",
    "model.compile(optimizer=opt,loss=mse, metrics='accuracy')\n",
    "model.fit(X_train_scaled,y_train,epochs=200,validation_split=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22/22 [==============================] - 0s 4ms/step\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean squared error: 2.2688609064137437\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 2.2689 - accuracy: 0.0014\n",
      "accuracy test set: 0.0014245014172047377\n"
     ]
    }
   ],
   "source": [
    "print('mean squared error: {}'.format(mean_squared_error(y_pred,y_test)))\n",
    "print('accuracy test set: {}'.format(model.evaluate(X_test_scaled,y_test)[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "outliers=[]\n",
    "for i in range(len(y_pred)):\n",
    "    if y_pred[i]>9 or y_pred[i]<3.3:\n",
    "        outliers.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_no_outliers = y_test.drop(y_test.index[outliers])\n",
    "y_pred_no_outliers = np.delete(y_pred, (outliers), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\test\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2 score: 0.12989646064635962\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 350x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(3.5,3.5))\n",
    "sns.regplot(y_test,y_pred,scatter_kws={\"s\": 10})\n",
    "plt.xlabel('Experimental pIC50', fontsize='large', fontweight='bold')\n",
    "plt.ylabel('Predicted pIC50', fontsize='large', fontweight='bold')\n",
    "print('r2 score: {}'.format(r2_score(y_test_no_outliers,y_pred_no_outliers)))\n",
    "# plt.plot(np.array([0,10]),np.array([3.3,3.3]))\n",
    "# plt.plot(np.array([0,10]),np.array([9.5,9.5]))\n",
    "plt.savefig(\"Plots/pIC50(predicted&experimental)regplot.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\test\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 350x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(3.5,3.5))\n",
    "sns.residplot(y_test,y_pred,scatter_kws={\"s\": 10})\n",
    "plt.xlabel('Experimental pIC50', fontsize='large', fontweight='bold')\n",
    "plt.ylabel('Predicted pIC50', fontsize='large', fontweight='bold')\n",
    "plt.savefig(\"Plots/pIC50(predicted&experimental)residplot.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The r2 score without outliers is 0.13, which does not imply a strong correlation. This is expected as PubChem fingerprints is a less accurate molecular representation of the compounds compared to newer techniques.[⁷](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-017-0195-1) However, visually, the points are clustered around the linear regression line and there are a reasonable amount of outliers, so the model did fit the data to a reasonable extent. This script can be improved by using a more accurate molecular fingerprint and potentially using other models that can fit the data better."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}