# # Copyright (c) 2009, Novartis Institutes for BioMedical Research Inc. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above # copyright notice, this list of conditions and the following # disclaimer in the documentation and/or other materials provided # with the distribution. # * Neither the name of Novartis Institutes for BioMedical Research Inc. # nor the names of its contributors may be used to endorse or promote # products derived from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR # A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT # OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY # THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # # Created by Greg Landrum and Anna Vulpetti, March 2009 from rdkit import Chem from rdkit import DataStructs from CreateFps import GetMolFingerprint from rdkit.ML.KNN.KNNRegressionModel import KNNRegressionModel from rdkit.RDLogger import logger logger=logger() import sys # nameField is the name of the property (from the SD file) that has molecule # names...If the molecules have names in the first row of the file, use "_Name" nameField = 'Compound_orig' #nameField = '_Name' # propField is the name of the property (from the SD file) you want to generate # predictions for propField='chemical_shift_1' weightedAverage=True import types,copy from optparse import OptionParser,Option,OptionValueError def check_floatlist(option,opt,value): try: v = eval(value) if type(v) not in (types.ListType,types.TupleType): raise ValueError v = [float(x) for x in v] except ValueError: raise OptionValueError("option %s : invalid float list value: %r"%( opt,value)) return v class MyOption(Option): TYPES=Option.TYPES+("floatlist",) TYPE_CHECKER = copy.copy(Option.TYPE_CHECKER) TYPE_CHECKER["floatlist"] = check_floatlist parser=OptionParser("distance predict",version='%prog',option_class=MyOption) parser.add_option('--maxPathLength','--max',default=8,type=int, help='maximum length path for the fingerprint') parser.add_option('--similarityThreshold','--sim',default=[0.9],type='floatlist', help='threshold for similarity') parser.add_option('--numNeighbors','--num','-n','-k',default=50,type=int, help='number of neighbors to consider') parser.add_option('--neighborsFile','--nbrs',default='', help='name of an output file to hold the neighbor lists') parser.add_option('--scan',default=False,action="store_true") if __name__=='__main__': options,args = parser.parse_args() outF = file(args[-1],'w+') logger.info('reading training molecules and generating fingerprints') suppl = Chem.SDMolSupplier(args[0]) train=[] for i,mol in enumerate(suppl): if not mol: continue smi = Chem.MolToSmiles(mol,True) nm = mol.GetProp(nameField) property = float(mol.GetProp(propField)) fp = GetMolFingerprint(mol,options.maxPathLength) train.append((nm,smi,fp,property)) logger.info(' got %d molecules'%len(train)) if len(args)>2: suppl = Chem.SDMolSupplier(args[1]) haveTest=True logger.info('reading testing molecules and generating fingerprints') test=[] for i,mol in enumerate(suppl): if not mol: continue smi = Chem.MolToSmiles(mol,True) nm = mol.GetProp(nameField) if mol.HasProp(propField): property = float(mol.GetProp(propField)) else: property=0 fp = GetMolFingerprint(mol,options.maxPathLength) test.append((nm,smi,fp,property)) logger.info(' got %d molecules'%len(test)) else: haveTest=False test=train results=[None]*len(test) for i in range(len(test)): results[i] = [None]*len(options.similarityThreshold) if options.neighborsFile: nbrFile=file(options.neighborsFile,'w+') print >>nbrFile,'ID|CompoundName|CompoundSmiles|NeighborName|NeighborSmiles|NeighborShift|Similarity' id=1 else: nbrFile=None for j,thresh in enumerate(options.similarityThreshold): if not haveTest: logger.info('Doing cross validation with threshold %.2f'%thresh) else: logger.info('Doing prediction with threshold %.2f'%thresh) for i in range(len(test)): if not haveTest: localTrain=[train[x] for x in range(len(train)) if x!=i] else: localTrain=train localTest=test[i] mdl = KNNRegressionModel(options.numNeighbors,[], lambda x,y,*args: 1-DataStructs.DiceSimilarity(x[-2],y[-2]), radius=1.-thresh) mdl.SetTrainingExamples(localTrain) nbrs=[] pred = mdl.PredictExample(localTest,weightedAverage=weightedAverage, neighborList=nbrs) nm,smi,fp,prop = test[i] if nbrFile: for dist,data in nbrs: if data is None: continue nnm,nsmi,nfp,nproperty = data outRow=[str(id),nm,smi,nnm,nsmi,str(nproperty),str(dist-1.)] id+=1 print >>nbrFile,'|'.join(outRow) nbrs = [x for x in nbrs if x[1] is not None] results[i][j]=(nm,smi,prop,pred,len(nbrs)) if not (i+1)%100: logger.info('Done %d molecules'%(i+1)) logger.info(' done') numNeighbors = options.numNeighbors maxPathLength = options.maxPathLength-1 logger.info('creating output file') headers=['name','smiles','shift'] for thresh in options.similarityThreshold: headers.append('predShift_%(maxPathLength)d_%(numNeighbors)d_%(thresh).2f'%locals()) headers.append('dPred_%(maxPathLength)d_%(numNeighbors)d_%(thresh).2f'%locals()) headers.append('nbrs_%(maxPathLength)d_%(numNeighbors)d_%(thresh).2f'%locals()) print >>outF,'|'.join(headers) for i in range(len(test)): nm=results[i][0][0] smi=results[i][0][1] prop=results[i][0][2] row = [nm,smi,str(prop)] for j in range(len(options.similarityThreshold)): nbrs=results[i][j][4] pred=results[i][j][3] row.append(str(pred)) row.append(str(abs(prop-pred))) row.append(str(nbrs)) print >>outF,'|'.join(row)