diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..a842fec
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,113 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+*.pyc
+
+# C extensions
+*.so
+
+*.o
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+doc/_build/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+.DS_Store
+no_ortho/
+ortho/
+*.zip
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..94a9ed0
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
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diff --git a/README.rst b/README.rst
new file mode 100644
index 0000000..86fdd15
--- /dev/null
+++ b/README.rst
@@ -0,0 +1,120 @@
+Prediction IncluDinG INactivity (PIDGIN) Version 3
+========================================
+
+|License|
+
+Author : Lewis Mervin, lhm30@cam.ac.uk
+
+Supervisor : Dr. A. Bender
+
+Protein target prediction using `Random Forests`_ (RFs) trained on bioactivity data from PubChem_ (extracted 07/06/18) and ChEMBL_ (version 24), using the RDKit_ and Scikit-learn_, which employ a modification of the reliability-density neighbourhood Applicability Domain (AD) analysis by Aniceto [1]_. This project is the sucessor to PIDGIN `version 1`_ [2]_ and PIDGIN `version 2`_ [3]_.
+
+* Molecular Descriptors : `2048bit Rdkit Extended Connectivity FingerPrints`_ (ECFP) [4]_
+* Algorithm: `Random Forests`_ with dynamic number of trees (see docs for details), class weight = 'balanced', sample weight = ratio Inactive:Active
+* Models generated at four different cut-off's: 100μM, 10μM, 1μM and 0.1μM
+* Models generated both with and without mapping to orthologues
+* Pathway information from `NCBI BioSystems`_ 
+* Disease information from `DisGeNET`_
+
+Details for sizes across all activity cut-off's:
+
++------------------------------------------------+-------------------------+---------------------------+
+|                                                | Without orthologues     | With orthologues          |
++================================================+=========================+===========================+
+| Distinct Models                                | 10,446                  | 14,678                    |
++------------------------------------------------+-------------------------+---------------------------+
+| Distinct Targets [exhaustive total]            | 7,075 [7,075]           | 16,623 [60,437]           |
++------------------------------------------------+-------------------------+---------------------------+
+| Total Bioactivities Over all models            | 39,424,168              | 398,340,769               |
++------------------------------------------------+-------------------------+---------------------------+
+| Actives                                        | 3,204,038               | 35,009,629                |
++------------------------------------------------+-------------------------+---------------------------+
+| Inactives [Of which are Sphere Exclusion (SE)] | 36,220,130 [27,435,133] | 363,331,140 [248,782,698] |
++------------------------------------------------+-------------------------+---------------------------+
+
+Full details on all models are provided in the uniprot_information.txt files in the orthologue and no_orthologue directories
+
+INSTRUCTIONS
+==========================================================================================
+
+Development and documentation occurs on GitHub_.
+
+Install with Conda
+----------------------
+
+Follow these steps on Linux/OSX:
+
+1. ``Download and install Anaconda2 for Python 2.7 from https://www.continuum.io/downloads``
+
+2. Open terminal in Mac/Linux and run ``conda create -c keiserlab -c rdkit -c sdaxen --name pidgin3_env python=2.7 pip e3fp scikit-learn pydot graphviz``
+
+* N.B. Rdkit may not import on some systems due to a bug. If this happens upgrade to the latest version of conda before creating the above environment using: ``conda update conda``
+
+3. Now run: ``source activate pidgin3_env`` (This activates the PIDGINv3 virtual environment. N.B This is required for each new terminal session in order to run PIDGIN in the future)
+
+4. Now run: ``pip install standaridser`` [Installs the IMI eTOX `flatkinson standardiser`_ (replaces ChemAxon's standardizer used in previous PIDGIN versions)]
+
+5. Navigate the directory you wish to install PIDGINv3 and in Mac/Linux terminal run ``git clone https://github.com/lhm30/PIDGINv3/`` (recommended) or download/extract the zip from `GitHub`_ webpage (not recommended due to inability to pull updates)
+
+6. Download and unzip `no_ortho.zip`_ into the PIDGINv3 main directory from `https://tinyurl.com/no-ortho`_ (leave all files within data compressed)
+
+* N.B Depending on bandwidth, Step 6 may take some time
+
+
+IMPORTANT
+==========================================================================================
+
+*	You MUST download the models before running!
+*	The program recognises as input line-separated SMILES in either .smi/.smiles or .sdf format
+*	If the SMILES input contains data additional to the SMILES string, the first entries after the SMILES are automatically interpreted as identifiers (see the `OpenSMILES specification <http://opensmiles.org/opensmiles.html>`_ §4.5) - although there are options to change this behaviour
+*	Molecules are automatically  standardized when running models (can be turned off)
+*	Do not modify the 'pkls', 'ad_data' etc. names or directories
+*	cytotox_library.csv and nontoxic_background.csv are included for testing purposes
+*	For installation and usage instructions, see the `documentation <http://pidginv3.readthedocs.io>`_.
+
+
+License
+-------
+
+PIDGINv3 is available under the `GNU General Public License v3.0
+<https://www.gnu.org/licenses/gpl.html>`_ (GPLv3).
+
+
+References
+----------
+
+.. [1] |aniceto|
+.. [2] |mervin2015|
+.. [3] |mervin2018|
+.. [4] |rogers|
+
+
+.. _Random Forests: http://scikit-learn.org/0.19/modules/generated/sklearn.ensemble.RandomForestClassifier.html
+.. _PubChem: https://pubchem.ncbi.nlm.nih.gov/
+.. _ChEMBL: https://www.ebi.ac.uk/chembl/
+.. _RDKit: http://www.rdkit.org
+.. _Scikit-learn: http://scikit-learn.org/
+.. _version 1: https://github.com/lhm30/PIDGIN
+.. _version 2: https://github.com/lhm30/PIDGINv2
+.. _no_ortho.zip : https://tinyurl.com/no-ortho
+.. _https://tinyurl.com/no-ortho : https://tinyurl.com/no-ortho
+.. _2048bit Rdkit Extended Connectivity FingerPrints: http://www.rdkit.org/docs/GettingStartedInPython.html#morgan-fingerprints-circular-fingerprints
+.. _NCBI BioSystems: https://www.ncbi.nlm.nih.gov/Structure/biosystems/docs/biosystems_about.html
+.. _DisGeNET: http://www.disgenet.org/web/DisGeNET/menu/dbinfo
+.. |aniceto| replace:: Aniceto, N, et al. A novel applicability domain technique for mapping predictive reliability across the chemical space of a QSAR: Reliability-density neighbourhood. *J. Cheminform.* **8**: 69 (2016). |aniceto_doi|
+.. |aniceto_doi| image:: https://img.shields.io/badge/doi-10.1186%2Fs13321--016--0182--y-blue.svg
+    :target: https://doi.org/10.1186/s13321-016-0182-y
+.. |mervin2015| replace:: Mervin, L H., et al. Target prediction utilising negative bioactivity data covering large chemical space. *J. Cheminform.* **7**: 51 (2015). |mervin2015_doi|
+.. |mervin2015_doi| image:: https://img.shields.io/badge/doi-10.1186%2Fs13321--015--0098--y-blue.svg
+    :target: https://doi.org/10.1186/s13321-015-0098-y
+.. |mervin2018| replace:: Mervin, L H., et al. Orthologue chemical space and its influence on target prediction. *Bioinformatics.* **34**: 72–79 (2018). |mervin2018_doi|
+.. |mervin2018_doi| image:: https://img.shields.io/badge/doi-10.1093%2Fbioinformatics%2Fbtx525-blue.svg
+    :target: https://doi.org/10.1093/bioinformatics/btx525
+.. |rogers| replace:: Rogers D & Hahn M. Extended-connectivity fingerprints. *J. Chem. Inf. Model.* **50**: 742-54 (2010). |rogers_doi|
+.. |rogers_doi| image:: https://img.shields.io/badge/doi-10.1021/ci100050t-blue.svg
+    :target: http://dx.doi.org/10.1021/ci100050t
+.. _GitHub: https://github.com/lhm30/PIDGINv3
+.. _flatkinson standardiser: https://github.com/flatkinson/standardiser
+.. _models.zip: 
+.. |license| image:: https://img.shields.io/badge/license-GPLv3-blue.svg
+   :target: https://github.com/lhm30/PIDGINv3/blob/master/LICENSE.txt
diff --git a/predict.py b/predict.py
new file mode 100644
index 0000000..6cde94c
--- /dev/null
+++ b/predict.py
@@ -0,0 +1,436 @@
+#Author : Lewis Mervin lhm30@cam.ac.uk
+#Supervisor : Dr. A. Bender
+#All rights reserved 2018
+#Protein Target Prediction using on SAR data from PubChem and ChEMBL_24
+#Molecular Descriptors : 2048bit circular Binary Fingerprints (Rdkit) - ECFP_4
+#Dependencies : rdkit, sklearn, standardiser
+
+### predict.py ###
+#Output a matrix of probabilities [computed as the mean predicted class probabilities of
+#the trees in the forest (where the class probability of a single tree is the fraction of 
+#samples of the same class in a leaf)], or user-specified Random probability thresholds to
+#produce binary predictions for an input list of smiles/sdfs. Predictions are generated 
+#for the [filtered] models using a reliability-density neighbourhood Applicability Domain
+#(AD) analysis from: doi.org/10.1186/s13321-016-0182-y
+
+#libraries
+import os
+import sys
+import bz2
+import zipfile
+import cPickle
+import glob
+import time
+import math
+import numpy as np
+from rdkit import Chem
+from rdkit.Chem import AllChem
+from rdkit import DataStructs
+from rdkit.Chem import Descriptors
+from scipy.stats import percentileofscore
+import multiprocessing
+from multiprocessing import Pool
+from optparse import OptionParser
+
+#optionparser options
+parser = OptionParser()
+parser.add_option("-f", dest="inf", help="Input smiles or sdf file (required)", metavar="FILE")
+parser.add_option("-d", "--smiles_delim", default=' ', type=str, dest="delim", help="Input file (smiles) delimiter char (default: white space ' ')")
+parser.add_option("--smiles_column", default=0, type=int, dest="smicol", help="Input file (smiles) delimiter column (default: 0)")
+parser.add_option("--smiles_id_column", default=1, type=int, dest="idcol", help="Input file (smiles) ID column (default: 1)")
+parser.add_option("-o", dest="off", default=None, help="Optional output prediction file name", metavar="FILE")
+parser.add_option("-t", "--transpose", action="store_true", default=False, dest="transpose", help="Transpose output (rows are compounds, columns are targets)")
+parser.add_option("-n", "--ncores", default=1, type=int, dest="ncores", help="No. cores (default: 1)")
+parser.add_option("-b", "--bioactivity", default=None, type=str, dest="bioactivity", help="Bioactivity threshold (can use multiple split by ','. E.g. '100,10'")
+parser.add_option("-p", "--proba", default=None, type=float, dest="proba", help="RF probability threshold (default: None)")
+parser.add_option("--ad", default='90', type=str, dest="ad", help="Applicability Domain (AD) filter using percentile of weights [float]. Default: 90 (integer for percentile)")
+parser.add_option("--known_flag", action="store_true", default=False, dest="known", help="Set known activities (annotate duplicates betweem input to train with correct label)")
+parser.add_option("--orthologues", action="store_true", default=False, dest="ortho", help="Set to use orthologue bioactivity data in model generation")
+parser.add_option("--organism", dest="organism", default=None, type=str, help="Organism filter (multiple can be specified using commas ',')")
+parser.add_option("--target_class", dest="targetclass", default=None, type=str, help="Target classification filter")
+parser.add_option("--min_size", dest="minsize", default=10, type=int, help="Minimum number of actives used in model generation (default: 10)")
+parser.add_option("--performance_filter", default=None, type=str, dest="p_filt", help="Comma-seperated performance filtering using following nomenclature: validation_set[tsscv,l50so,l50po],metric[bedroc,roc,prauc,brier],performance_threshold[float]. E.g 'tsscv,bedroc,0.5'")
+parser.add_option("--se_filter", action="store_true", default=False, dest="se_filt", help="Optional setting to restrict to models which do not require Sphere Exclusion (SE)")
+parser.add_option("--training_log", action="store_true", default=False, dest="train_log", help="Optional setting to add training_details to the prediction file (large increase in output file size)")
+parser.add_option("--ntrees", dest="ntrees", default=None, type=int, help="Specify the minimum number of trees for warm-start random forest models (N.B Potential large latency/memory cost)")
+parser.add_option("--preprocess_off", dest="preproc", action="store_false", default=True, help="Turn off preprocessing using the flatkinson (eTox) standardizer (github.com/flatkinson/standardiser), size filter (100 >= Mw >= 1000 and organic mol check (C count >= 1)")
+parser.add_option("--std_dev", dest="std", action="store_true", default=False, help="Turn on matrix calculation for the standard deviation of prediction across the trees in the forest")
+parser.add_option("--percentile", dest="percentile", action="store_true", default=False, help="Turn on matrix calculation for the percentile of AD compounds")
+
+(options, args) = parser.parse_args()
+#if tab delimited then set to \t
+if options.delim == 'tab': options.delimcol1 = '\t'
+
+def introMessage():
+	print '=============================================================================================='
+	print ' Author: Lewis Mervin\n Email:  lhm30@cam.ac.uk\n Supervisor: Dr. A. Bender'
+	print ' Address: Centre For Molecular Informatics, Dept. Chemistry, Lensfield Road, Cambridge CB2 1EW'
+	print '==============================================================================================\n'
+	return
+
+#check smiles of sdf (if not quit)	
+def check_Input():
+	global options
+	extension = options.inf.split('.')[-1]
+	if extension not in ['smi','smiles','sdf']:
+		print ' Warning [-f]: File type not "smi", "smiles" or "sdf". Interpreting input as SMILES'
+	return extension
+
+#check input & set OS directory pointers
+def check_set_working_env():
+	global options
+	if os.name == 'nt': sep = '\\'
+	else: sep = '/'
+	input_extension = check_Input()
+	try:
+		ad_settings = int(options.ad)
+		if 0 >= ad_settings >= 100: raise ValueError
+	except ValueError:
+		print ' Input Error [--ad]: Percentile weight not integer between 0-100%. Please Check parameters'
+		sys.exit()
+	pidgin_dir = os.path.dirname(os.path.abspath(__file__))
+	if options.ortho:
+		mod_dir = pidgin_dir + sep + 'data' + sep + 'ortho' + sep
+		if not os.path.isdir(mod_dir):
+			print ' Orthologue Error [--orthologues]: Orthologues directory is not present. Please download from here: '
+	else: mod_dir = pidgin_dir + sep + 'data' + sep + 'no_ortho' + sep
+	return input_extension, sep, pidgin_dir, mod_dir, ad_settings
+
+#filter models using user specification supplied via option parser
+def get_Models():
+	global mod_dir, sep, options
+	target_count = 0
+	model_info = [l.split('\t') for l in open(mod_dir + 'training_log.txt').read().splitlines()]
+	if options.ntrees: model_info = [m[:2]+[str(options.ntrees)]+m[3:] if idx > 0 else m \
+	for idx, m in enumerate(model_info)]
+	model_info = {l[0] : l for l in model_info}	
+	uniprot_info = [i.split('\t') for i in open(mod_dir + 'uniprot_information.txt').read().splitlines()[1:]]
+	mid_uniprots = dict()
+	if options.p_filt:
+		val_dict = {'tsscv':7, 'l50po':12, 'l50so':17}
+		metric_dict = dict(zip(['bedroc','roc','prauc','brier'],range(4)))
+		try:
+			(validation,metric,perf_threshold) = [pf.lstrip() for pf in options.p_filt.split(',')]
+			train_row_idx = val_dict[validation] + metric_dict[metric]
+			perf_threshold = float(perf_threshold)
+			print ' Filtering models for a minimum ' + metric + ' performance of ' \
+			 + str(perf_threshold) + ' during ' + validation + ' validaiton'
+		except (KeyError,ValueError):
+			print ' Input Error [--performance_filter]: Use format ' \
+			'validation_set[tsscv,l50so,l50po],metric[bedroc,roc,prauc,brier],' \
+			'threshold[float].\n E.g "bedroc,tsscv,0.5"\n...exiting'
+			sys.exit()
+	if options.organism: orgs = map(str.lower, options.organism.split(','))
+	for row in uniprot_info:
+		#filter bioactivity/organism/targetclass/minsize/se/performance (if set)
+		if options.bioactivity and row[8] not in options.bioactivity.split(','): continue
+		if options.organism and (row[4] == '' \
+		or not any([org.lstrip() in row[4].lower() for org in orgs])): continue
+		if options.targetclass and row[3] not in options.targetclass: continue
+		if sum(map(int,row[9:11])) < options.minsize: continue
+		if options.se_filt and int(row[13]) > 0: continue
+		if options.p_filt:
+			try:
+				if float(model_info[row[-1]][train_row_idx].split(',')[0]) < perf_threshold: continue
+			except ValueError: continue
+		#if passes all filters then add to mid->uniprot dictionary
+		try: mid_uniprots[row[-1]].append(row)
+		except KeyError: mid_uniprots[row[-1]] = [row]
+		target_count +=1
+	if len(mid_uniprots) == 0:
+		print ' Warning: No eligable models using current filters...exiting'
+		sys.exit()
+	return mid_uniprots, model_info, target_count
+
+#preprocess rdkit molecule
+def preprocessMolecule(inp):
+	def checkC(mm):
+		mwt = Descriptors.MolWt(mm)
+		for atom in mm.GetAtoms():
+			if atom.GetAtomicNum() == 6 and 100 <= mwt <= 1000: return True
+		return False
+	def checkHm(mm):
+		for atom in mm.GetAtoms():
+			if atom.GetAtomicNum() in [2,10,13,18]: return False
+			if 21 <= atom.GetAtomicNum() <= 32: return False
+			if 36 <= atom.GetAtomicNum() <= 52: return False
+			if atom.GetAtomicNum() >= 54: return False
+		return True
+	try: std_mol = standardise.run(inp)
+	except standardise.StandardiseException: return None
+	if not std_mol or checkHm(std_mol) == False or checkC(std_mol) == False: return None
+	else: return std_mol
+
+#preprocess exception to catch
+class MolFromSmilesError(Exception):
+    'raise due to "None" from Chem.MolFromSmiles'
+
+#preprocess exception to catch
+class PreprocessViolation(Exception):
+    'raise due to preprocess violation'
+
+#calculate 2048bit morgan fingerprints, radius 2, for smiles or sdf input
+def calcFingerprints(input,qtype='smiles'):
+	if qtype == 'smiles': m = Chem.MolFromSmiles(input)
+	else: m = input
+	if not m: raise MolFromSmilesError(' None mol in function')
+	if options.preproc:
+		m = preprocessMolecule(m)
+		if not m: raise PreprocessViolation(' Molecule preprocessing violation')
+	fp = AllChem.GetMorganFingerprintAsBitVect(m,2, nBits=2048)
+	binary = fp.ToBitString()
+	if qtype == 'sdf': return Chem.MolToSmiles(m), map(int,list(binary)), fp
+	else: return map(int,list(binary)), fp
+
+#calculate fingerprints for chunked array of smiles
+def arrayFP(inp):
+	outfp = []
+	outmol = []
+	outsmi_id = []
+	for idx, i in inp:
+		i = i.split(options.delim)
+		try:
+			fp, mol = calcFingerprints(i[options.smicol])
+			outfp.append(fp)
+			outmol.append(mol)
+			try: outsmi_id.append(i[options.idcol])
+			except IndexError: outsmi_id.append(i[options.smicol])
+		except PreprocessViolation: print ' SMILES preprocessing violation (line ' + str(idx+1) + ' will be removed): ' + i[options.smicol]
+		except MolFromSmilesError: print ' SMILES parse error (line ' + str(idx+1) + '): ' + i[options.smicol]
+	return outfp, outmol, outsmi_id
+
+#import user smiles query
+def importQuerySmiles(in_file):
+	query = open(in_file).read().splitlines()
+	query = zip(range(len(query)),query)
+	matrix = np.empty((len(query), 2048), dtype=np.uint8)
+	smiles_per_core = int(math.ceil(len(query) / options.ncores)+1)
+	chunked_smiles = [query[x:x+smiles_per_core] for x in xrange(0, len(query), smiles_per_core)]
+	pool = Pool(processes=options.ncores)  # set up resources
+	jobs = pool.imap(arrayFP, chunked_smiles)
+	current_end = 0
+	processed_mol = []
+	processed_id = []
+	for i, result in enumerate(jobs):
+		percent = (float(i)/float(len(chunked_smiles)))*100 + 1
+		sys.stdout.write(' Processing Molecules: %3d%%\r' % percent)
+		sys.stdout.flush()
+		matrix[current_end:current_end+len(result[0]), :] = result[0]
+		current_end += len(result[0])
+		processed_mol += result[1]
+		processed_id += result[2]
+	pool.close()
+	pool.join()
+	print
+	return matrix[:current_end], processed_mol, processed_id
+	
+#preprocess exception to catch
+class SdfNoneMolError(Exception):
+    'raised due to "None" mol during enumeration through Chem.SDMolSupplier'
+
+#import user query for sdf
+def importQuerySDF(in_file):
+	outfp = []
+	outid= []
+	outmol = []
+	query = Chem.SDMolSupplier(in_file)
+	for idx, m in enumerate(suppl):
+		sys.stdout.write(' Importing SDF file. Compound number: %s\r' % idx)
+		sys.stdout.flush()
+		try:
+			if not m: raise SdfNoneMolError('None mol')
+			smi, fp, mol = calcFingerprints(m,qtype='sdf')
+			try: outid.append(m.GetProp("_Name"))
+			except KeyError: outid.append(smi)
+			outfp.append(fp)
+			outmol.append(mol)
+		except SdfNoneMolError: print ' SDF parse error (compound index: ' + str(idx) + ')'
+	print
+	return np.array(outfp,dtype=np.uint8),outmol,outid
+
+#unzip a pkl model
+def open_Model(mod):
+	global mod_dir, options
+	with bz2.BZ2File(mod_dir + 'pkls' + sep + mod + '.pkl.bz2', 'rb') as bzfile:
+		clf = cPickle.load(bzfile)
+		#if set, increase number of trees in forest
+		if options.ntrees and clf.n_estimators < options.ntrees:
+			clf.set_params(n_estimators=options.ntrees)
+	return clf
+
+#import the training data similarity, bias and standard deviation file for given model
+def getAdData(model_name):
+	global mod_dir, sep
+	actual_mid = model_name.split('/')[-1].split('.pkl.bz2')[0]
+	ad_file = mod_dir + 'ad_analysis' + sep + actual_mid + '.pkl.bz2'
+	with bz2.BZ2File(ad_file, 'rb') as bzfile:
+		ad_data = cPickle.load(bzfile)
+	return ad_data
+
+#perform AD analysis using full similarity weighted threshold [similarity/(bias * std_dev]
+#adapted from DOI:10.1186/s13321-016-0182-y
+def doSimilarityWeightedAdAnalysis(model_name):
+	global rdkit_mols, ad_settings
+	ad_idx = []
+	known = []
+	ad_data = getAdData(model_name)
+	required_threshold = np.percentile(ad_data[:,5],ad_settings)
+	for mol_idx, m in enumerate(rdkit_mols):
+		ad_flag = False
+		#only check for known compounds if set in options (True means dont check)
+		if options.known: k_flag = False
+		else: k_flag = True
+		for training_instance in ad_data:
+			sim = DataStructs.TanimotoSimilarity(m,training_instance[0])
+			if sim == 1.0 and k_flag == False:
+				known.append([mol_idx,training_instance[1]])
+				k_flag = True
+			weight = sim/(training_instance[2]*training_instance[3])
+			if weight >= required_threshold and ad_flag != True:
+				ad_idx.append(mol_idx)
+				ad_flag = True
+			#if compound is in AD and no need to check accross all comps for known then break
+			if k_flag == True and ad_flag == True: break
+	return ad_idx, np.array(known)
+
+#return percentile AD analysis for [similarity/(bias * std_dev] vs training data
+def doPercentileSimilarityWeightedAdAnalysis(model_name):
+	global rdkit_mols,ad_settings
+	ret = []
+	ad_data = getAdData(model_name)
+	for mol_idx, m in enumerate(rdkit_mols):
+		percentiles = []
+		for training_instance in ad_data:
+			sim = DataStructs.TanimotoSimilarity(m,training_instance[0])
+			weight = sim/(training_instance[2]*training_instance[3])
+			percentiles.append(percentileofscore(ad_data[:,5],weight))
+		ret.append(max(percentiles))
+	return ret
+
+#calculate standard deviation for an input compound
+def getStdDev(clf, querymatrix):
+	std_dev = []
+	for tree in range(len(clf.estimators_)):
+		std_dev.append(clf.estimators_[tree].predict_proba(querymatrix)[:,1])
+	std_dev = np.clip(np.std(std_dev,axis=0),0.001,None)
+	return std_dev
+
+#raw or binary prediction worker
+def doTargetPrediction(model_name):
+	global ad_settings
+	if options.percentile: return model_name, doPercentileSimilarityWeightedAdAnalysis(model_name)
+	clf = open_Model(model_name)
+	ret = np.zeros(len(querymatrix))
+	ret.fill(np.nan)
+	#percentile ad analysis calculated from [near_neighbor_similarity/(bias*deviation)]
+	try:
+		ad_idx, known = doSimilarityWeightedAdAnalysis(model_name)
+	except: return model_name, ret
+	#if no mols in AD then return
+	if len(ad_idx) == 0: return model_name, ret
+	probs = clf.predict_proba(querymatrix[ad_idx])[:,1].clip(0.001,0.999)
+	#return the standard deviation if turned on
+	if options.std:
+		std_dev = getStdDev(clf,querymatrix)
+		ret[ad_idx] = std_dev[ad_idx]
+		return model_name, ret
+	#will only have known if was set on
+	if len(known) > 0: probs[known[:,0]] = known[:,1]
+	if threshold: ret[ad_idx] = map(int,probs > threshold)
+	else: ret[ad_idx] = probs
+	return model_name, ret
+
+#prediction runner for prediction or standard deviation calculation
+def performTargetPrediction(models):
+	prediction_results = []
+	pool = Pool(processes=options.ncores, initializer=initPool, initargs=(querymatrix,options.proba,))
+	jobs = pool.imap_unordered(doTargetPrediction, models)
+	for i, result in enumerate(jobs):
+		percent = (float(i)/float(len(models)))*100 + 1
+		sys.stdout.write(' Performing Classification on Query Molecules: %3d%%\r' % percent)
+		sys.stdout.flush()
+		if prediction_results is not None: prediction_results.append(result)
+	pool.close()
+	pool.join()
+	return prediction_results
+
+#write out normal results (rows are targets, columns are compounds)
+def writeOutResults(results):
+	global query_id, mid_uniprots, model_info, options
+	out_data = []
+	for mid, preds in results:
+		for uniprot_rows in mid_uniprots[mid]:
+			if options.train_log: out_data += [uniprot_rows + model_info[mid][1:] + list(preds)]
+			else: out_data += [uniprot_rows + list(preds)]
+	timestr = time.strftime("%Y%m%d-%H%M%S")
+	if not options.off: output_name = options.inf + '_out_predictions_' + timestr + '.txt'
+	else: output_name = options.off + '_out_predictions_' + timestr + '.txt'
+	print ' Writing predictions to file: ' + output_name
+	out_file = open(output_name, 'w')
+	header = 'Uniprot\tName\tGene_ID\tProtein_Classification\tOrganism' \
+	'\tPDB_IDs\tDisGeNET_0.06\tChEMBL_First_Publication\tActivity_Threshold' \
+	'\tActives\tOrthologue_Actives\tInactives\tOrthologue_Inactives' \
+	'\tSphere_Exclusion_Inactives\tRatio\tModel_ID\t'
+	if options.train_log: out_file.write(header + \
+	'\t'.join(map(str,model_info['MODEL_ID'][1:])) + '\t' + '\t'.join(map(str,query_id)) + '\n')
+	else:
+		out_file.write(header + '\t'.join(map(str,query_id)) + '\n')
+	for row in out_data: out_file.write('\t'.join(map(str,row)) + '\n')
+	out_file.close()
+	return
+
+#write out transposed results (columns are targets, rows are compounds)
+def writeOutTransposed(results):
+	global query_id, mid_uniprots, model_info, options
+	out_data = [['Uniprot'] + query_id]
+	for mid, preds in results:
+		for uniprot_rows in mid_uniprots[mid]:
+			out_data += [[uniprot_rows[0]] + list(preds)]
+	timestr = time.strftime("%Y%m%d-%H%M%S")
+	if not options.off: output_name = options.inf + '_out_predictions_' + timestr + '.txt'
+	else: output_name = options.off + 'lx -l_out_predictions_' + timestr + '.txt'
+	print ' Writing out transposed predictions to file: ' + output_name
+	out_file = open(output_name, 'w')
+	out_data = np.transpose(out_data)
+	for row in out_data: out_file.write('\t'.join(map(str,row)) + '\n')
+	out_file.close()
+	return
+
+#nt (Windows) compatibility initializer for the pool
+def initPool(querymatrix_, threshold_):
+	global querymatrix, threshold
+	querymatrix = querymatrix_
+	threshold = threshold_
+
+#main
+if __name__ == '__main__':
+	introMessage()
+	print ' Predicting Targets for input: ' + options.inf
+	print ' Using ' + str(options.ncores) + ' core(s)'
+	if options.ntrees: print 'Latency warning: Number of trees will be increased to minimum: ' + str(options.ntrees)
+	print ' Using bioactivity thresholds (IC50/EC50/Ki/Kd) of : ' + str(options.bioactivity)
+	print ' Using orthologues: ' + str(options.ortho)
+	if options.organism: print ' Organism filter : ' + options.organism
+	if options.targetclass: print ' Target class filter : ' + options.targetclass
+	if options.minsize: print ' Minimum number of actives in training : ' + str(options.minsize)
+	if options.se_filt: print ' Filtering out models with Sphere Exclusion (SE)'
+	if options.train_log: print ' Training log will be added to output'
+	#set up environment
+	input_extension, sep, pidgin_dir, mod_dir, ad_settings = check_set_working_env()
+	#if preprocessing import standardizer
+	if options.preproc: from standardiser import standardise
+	#gather the models required and their information
+	mid_uniprots, model_info, target_count = get_Models()
+	print ' Total number of protein targets: ' + str(target_count)
+	print ' Total number of distinct models: ' + str(len(mid_uniprots))
+	print ' Using p(activity) threshold of: ' + str(options.proba)
+	print ' Importing query (calculating ECFP_4 finerprints)'
+	#import user query files
+	if input_extension == 'sdf': querymatrix,rdkit_mols,query_id = importQuerySDF(options.inf)
+	else:  querymatrix,rdkit_mols,query_id = importQuerySmiles(options.inf)
+	print ' Total number of query molecules : ' + str(len(querymatrix))
+	#perform target prediction on (filtered) models (using model ids)
+	prediction_results = performTargetPrediction(mid_uniprots.keys())
+	#write out
+	if options.transpose: writeOutTransposed(prediction_results)
+	else: writeOutResults(prediction_results)
\ No newline at end of file