AMES_Test-Part-1-genotoxicity-data.ipynb

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        "<a href=\"https://colab.research.google.com/github/stawiskm/QSAR_Modelbuilding_amesTest/blob/main/AMES_Test-Part-1-genotoxicity-data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wSFbIMb87cHu"
      },
      "source": [
        "# **Computational Ames test [Part 1] Data collection**\n",
        "\n",
        "Marc Jermann\n",
        "\n",
        "In this Jupyter notebook, we will be building a real-life **data science project**. Particularly, we will be building a machine learning model using the ChEMBL bioactivity data and other data sources.\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3iQiERxumDor"
      },
      "source": [
        "## **ChEMBL Database**\n",
        "\n",
        "The [*ChEMBL Database*](https://www.ebi.ac.uk/chembl/) is a database that contains curated bioactivity data of more than 2 million compounds. It is compiled from more than 76,000 documents, 1.2 million assays and the data spans 13,000 targets and 1,800 cells and 33,000 indications.\n",
        "[Data as of March 25, 2020; ChEMBL version 26]."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iryGAwAIQ4yf"
      },
      "source": [
        "## **Installing libraries**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "toGT1U_B7F2i"
      },
      "source": [
        "Install the ChEMBL web service package so that we can retrieve bioactivity data from the ChEMBL Database."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
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        "colab": {
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        },
        "id": "cJGExHQBfLh7",
        "outputId": "dbd76016-6f75-48b0-ca96-dbed073cf30c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: chembl_webresource_client in /usr/local/lib/python3.7/dist-packages (0.10.8)\n",
            "Requirement already satisfied: requests>=2.18.4 in /usr/local/lib/python3.7/dist-packages (from chembl_webresource_client) (2.23.0)\n",
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            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from chembl_webresource_client) (1.24.3)\n",
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            "Requirement already satisfied: itsdangerous>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from requests-cache~=0.7.0->chembl_webresource_client) (2.1.2)\n",
            "Requirement already satisfied: attrs<22.0,>=21.2 in /usr/local/lib/python3.7/dist-packages (from requests-cache~=0.7.0->chembl_webresource_client) (21.4.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from url-normalize<2.0,>=1.4->requests-cache~=0.7.0->chembl_webresource_client) (1.15.0)\n"
          ]
        }
      ],
      "source": [
        "! pip install chembl_webresource_client"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J0kJjL8gb5nX"
      },
      "source": [
        "## **Importing libraries**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "RXoCvMPPfNrv"
      },
      "outputs": [],
      "source": [
        "# Import necessary libraries\n",
        "import pandas as pd\n",
        "from chembl_webresource_client.new_client import new_client"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1FgUai1bfigC"
      },
      "source": [
        "## **Search for Target**\n",
        "\n",
        "Since the Ames test is performed using Salmonella typhimurium, this organism is our target."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7lBsDrD0gAqH"
      },
      "source": [
        "### **Target search for Salmonella typhimurium**"
      ]
    },
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              "  cross_references                                           organism  \\\n",
              "0               []  Salmonella enterica subsp. enterica serovar Ty...   \n",
              "1               []                                         Salmonella   \n",
              "2               []                                       Homo sapiens   \n",
              "3               []  Salmonella enterica subsp. enterica serovar Pa...   \n",
              "\n",
              "                    pref_name  score  species_group_flag target_chembl_id  \\\n",
              "0      Salmonella typhimurium   31.0               False        CHEMBL351   \n",
              "1                  Salmonella   15.0                True     CHEMBL614446   \n",
              "2  Protein NipSnap homolog 3A   15.0               False    CHEMBL3817722   \n",
              "3        Salmonella paratyphi   13.0               False     CHEMBL612293   \n",
              "\n",
              "                                   target_components     target_type  tax_id  \n",
              "0                                                 []        ORGANISM   90371  \n",
              "1                                                 []        ORGANISM     590  \n",
              "2  [{'accession': 'Q9UFN0', 'component_descriptio...  SINGLE PROTEIN    9606  \n",
              "3                                                 []        ORGANISM   54388  "
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          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# Target search for Salmonella typhimurium\n",
        "target = new_client.target\n",
        "target_query = target.search('Salmonella typhimurium')\n",
        "targets = pd.DataFrame.from_dict(target_query)\n",
        "targets.head(4)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y5OPfEALjAfZ"
      },
      "source": [
        "### **Select and retrieve activity data for Ames tests with *Salmonella typhimurium* (first entry)**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gSQ3aroOgML7"
      },
      "source": [
        "We will assign the first entry (which corresponds to the target organism, *Salmonella typhimurium*) to the ***selected_target*** variable "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "StrcHMVLha7u",
        "outputId": "b9f67f23-fabc-45bd-bf6f-e2cd5e7d40a9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'CHEMBL351'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "selected_target = targets.target_chembl_id[0]\n",
        "selected_target"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GWd2DRalgjzB"
      },
      "source": [
        "Here, we will retrieve only bioactivity data for *Salmonella typhimurium* (CHEMBL351) that are reported as activity."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "LeFbV_CsSP8D"
      },
      "outputs": [],
      "source": [
        "activity = new_client.activity\n",
        "res = activity.filter(target_chembl_id=selected_target).filter(standard_type=\"Activity\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "RC4T-NEmSWV-"
      },
      "outputs": [],
      "source": [
        "df = pd.DataFrame.from_dict(res)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_xLDJ1a4_mW6"
      },
      "source": [
        "Now we keep only the entries that also have the keyword \"Ames\" in their **assay_description**. Either as \"Ames test\" or \"Ames assay\".\n",
        "Also, we keep only the entries that have an understandable **activity_comment**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "eNjYCiHCMJTW"
      },
      "outputs": [],
      "source": [
        "df_ames = df.loc[df['assay_description'].str.contains(' Ames ')]\n",
        "df_ames = df_ames.loc[df['activity_comment'].isin(['Non-toxic', 'Toxic','Non-Toxic'])]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5H4sSFAWhV9B"
      },
      "source": [
        "## **Data pre-processing of the data**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_GXMpFNUOn_8"
      },
      "source": [
        "### **Handling missing data**\n",
        "If any compounds has missing value for the **activity_comment** column then drop it"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hkVOdk6ZR396",
        "outputId": "dbf0b67c-4726-434d-fd85-8f0d2a1150f9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Non-toxic    964\n",
            "Non-Toxic    404\n",
            "Toxic        298\n",
            "Name: activity_comment, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# frequency count of column activity_comment\n",
        "count = df_ames[\"activity_comment\"].value_counts()\n",
        "print(count)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y-qNsUlmjS25"
      },
      "source": [
        "Apparently, for this dataset there is no missing data. But there are two spellings for Non-toxic. This will be corrected in the next step."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "AcKVNh0tD4P_"
      },
      "outputs": [],
      "source": [
        "df_ames['activity_comment'] = df_ames['activity_comment'].replace({'Non-Toxic':'Non-toxic'})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vE0Vvo6ic3MI"
      },
      "source": [
        "### **Remove unneeded columns and duplicates**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "VICiiCtqc2ne"
      },
      "outputs": [],
      "source": [
        "selection = ['molecule_chembl_id', 'canonical_smiles', 'activity_comment']\n",
        "df_ames = df_ames[selection]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
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          "height": 424
        },
        "id": "FNVdtz3lIbyK",
        "outputId": "2669cb14-1e7f-4e73-e248-faeca8019212"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     molecule_chembl_id                                   canonical_smiles  \\\n",
              "389        CHEMBL398372  CC(C)(N)C(=O)N[C@H](COCc1ccccc1)c1nnnn1CCOC(=O...   \n",
              "420        CHEMBL137803  C=C1C(=O)O[C@@H]2C[C@@]3(C)CCCC(=C)[C@@H]3C[C@...   \n",
              "422        CHEMBL486423  C=C1CCC[C@]2(C)C[C@H]3OC(=O)[C@@H](C)[C@H]3C[C...   \n",
              "423          CHEMBL6466                                   O=c1ccc2ccccc2o1   \n",
              "424         CHEMBL24171                          COc1c2ccoc2cc2oc(=O)ccc12   \n",
              "...                 ...                                                ...   \n",
              "4768      CHEMBL4283853  COCCCOc1cc2c(cc1OC)-c1cc(=O)c(C(=O)O)cn1[C@H](...   \n",
              "4770      CHEMBL4284040           N#C/C(=C\\c1cccc2cnccc12)c1c[nH]c2ccccc12   \n",
              "4771      CHEMBL3945880  O=c1cc(NC2CCN(S(=O)(=O)C(F)(F)F)CC2)c2cc(C(c3c...   \n",
              "4793      CHEMBL4514379    N#Cc1cnc2ccc(-c3c(-c4ccc(F)c(Cl)c4)ncn3CCO)nn12   \n",
              "4797      CHEMBL4554511  CC(C)(C)OC(=O)Nc1ccc(-c2cc(C(=O)NCCc3cc(C(=O)N...   \n",
              "\n",
              "     activity_comment  \n",
              "389         Non-toxic  \n",
              "420             Toxic  \n",
              "422         Non-toxic  \n",
              "423         Non-toxic  \n",
              "424         Non-toxic  \n",
              "...               ...  \n",
              "4768        Non-toxic  \n",
              "4770        Non-toxic  \n",
              "4771        Non-toxic  \n",
              "4793        Non-toxic  \n",
              "4797        Non-toxic  \n",
              "\n",
              "[655 rows x 3 columns]"
            ],
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              "      <th></th>\n",
              "      <th>molecule_chembl_id</th>\n",
              "      <th>canonical_smiles</th>\n",
              "      <th>activity_comment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>389</th>\n",
              "      <td>CHEMBL398372</td>\n",
              "      <td>CC(C)(N)C(=O)N[C@H](COCc1ccccc1)c1nnnn1CCOC(=O...</td>\n",
              "      <td>Non-toxic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>420</th>\n",
              "      <td>CHEMBL137803</td>\n",
              "      <td>C=C1C(=O)O[C@@H]2C[C@@]3(C)CCCC(=C)[C@@H]3C[C@...</td>\n",
              "      <td>Toxic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>422</th>\n",
              "      <td>CHEMBL486423</td>\n",
              "      <td>C=C1CCC[C@]2(C)C[C@H]3OC(=O)[C@@H](C)[C@H]3C[C...</td>\n",
              "      <td>Non-toxic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>423</th>\n",
              "      <td>CHEMBL6466</td>\n",
              "      <td>O=c1ccc2ccccc2o1</td>\n",
              "      <td>Non-toxic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>424</th>\n",
              "      <td>CHEMBL24171</td>\n",
              "      <td>COc1c2ccoc2cc2oc(=O)ccc12</td>\n",
              "      <td>Non-toxic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>4768</th>\n",
              "      <td>CHEMBL4283853</td>\n",
              "      <td>COCCCOc1cc2c(cc1OC)-c1cc(=O)c(C(=O)O)cn1[C@H](...</td>\n",
              "      <td>Non-toxic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4770</th>\n",
              "      <td>CHEMBL4284040</td>\n",
              "      <td>N#C/C(=C\\c1cccc2cnccc12)c1c[nH]c2ccccc12</td>\n",
              "      <td>Non-toxic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4771</th>\n",
              "      <td>CHEMBL3945880</td>\n",
              "      <td>O=c1cc(NC2CCN(S(=O)(=O)C(F)(F)F)CC2)c2cc(C(c3c...</td>\n",
              "      <td>Non-toxic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4793</th>\n",
              "      <td>CHEMBL4514379</td>\n",
              "      <td>N#Cc1cnc2ccc(-c3c(-c4ccc(F)c(Cl)c4)ncn3CCO)nn12</td>\n",
              "      <td>Non-toxic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4797</th>\n",
              "      <td>CHEMBL4554511</td>\n",
              "      <td>CC(C)(C)OC(=O)Nc1ccc(-c2cc(C(=O)NCCc3cc(C(=O)N...</td>\n",
              "      <td>Non-toxic</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>655 rows × 3 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "df_ames=df_ames.drop_duplicates(subset=['molecule_chembl_id'], keep='last')\n",
        "df_ames"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WMVKOvlGCG-B"
      },
      "source": [
        "## **File handling**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fQ78N26Fg15T"
      },
      "source": [
        "Finally we will save the resulting data to a CSV file."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "nSNia7suXstR"
      },
      "outputs": [],
      "source": [
        "df_ames.to_csv('QSAR_ames_raw-data.csv', index=False)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "AMES_Test-Part-1-bioactivity-data.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.9.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}