forgot this file

build/utils/build_drug_desc.py

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+'''
+build drug descriptor table from drug table
+
+
+'''
+
+
+import argparse
+from rdkit import Chem
+from rdkit.Chem import AllChem
+#from rdkit.Chem import rdFingerprintGenerator
+import pandas as pd
+import numpy as np
+from mordred import Calculator, descriptors
+import multiprocessing
+
+
+def smiles_to_fingerprint(smiles):
+    '''
+    takes smiles nad create morgan fingerprint
+    '''
+    fdict = []
+    ##get morgan fingerprint
+    print('Computing morgan fingerprints for '+str(len(smiles))+' SMILES')
+ #   morgan_fp_gen = rdFingerprintGenerator.GetMorganGenerator(radius=2, fpSize=1024, useCountSimulation=False)
+    for s in smiles:
+       # print(s)
+        mol = Chem.MolFromSmiles(s)
+        try:
+            #this has been depracated despite being in Alex's original script
+            fingerprint = AllChem.GetMorganFingerprintAsBitVect(mol, radius=2, nBits=1024)  # update these parameters
+  #          fingerprint = morgan_fp_gen.GetFingerprint(mol)
+            #            vec2 = np.array(fp2)
+        except:
+            print('Cannot compute fingerprint for '+s)
+            continue
+        fingerprint_array = np.array(fingerprint)
+        fstr = ''.join([str(a) for a in fingerprint_array])
+        fdict.append({'smile':s,'descriptor_value':fstr,'structural_descriptor':'morgan fingerprint'})
+
+    return pd.DataFrame(fdict)#fingerprint_array
+
+
+def smiles_to_mordred(smiles,nproc=2):
+    '''
+    get descriptors - which ones?
+    '''
+    print('Computing mordred descriptors for '+str(len(smiles))+' SMILES')
+
+
+    mols = [Chem.MolFromSmiles(s) for s in smiles]
+    smols = []
+    ssmil = []
+    for i in range(len(mols)):
+        m = mols[i]
+        if m is not None:
+            smols.append(m)
+            ssmil.append(smiles[i])
+
+    calc = Calculator(descriptors, ignore_3D=True)
+    dd = calc.pandas(mols=smols, nproc=nproc, quiet=False, ipynb=False )
+    values = dd.columns
+    dd['smile'] = ssmil
+    ##reformat here
+    longtab = pd.melt(dd,id_vars='smile',value_vars=values)
+    longtab = longtab.rename({'variable':'structural_descriptor','value':'descriptor_value'},axis=1)
+    return longtab
+
+def main():
+    parser = argparse.ArgumentParser('Build drug descriptor table')
+    parser.add_argument('--drugtable',dest='drugtable')
+    parser.add_argument('--desctable',dest='outtable')
+
+    args  = parser.parse_args()
+
+    cores = multiprocessing.cpu_count()
+    ncors = cores-1
+    print("Running with "+str(ncors)+' out of '+str(cores)+' processors')
+    print('Adding drug table for '+args.drugtable)
+    tab = pd.read_csv(args.drugtable,sep='\t')
+
+    cansmiles = [a for a in set(tab.canSMILES) if str(a)!='nan']
+    #    isosmiles = list(set(tab.isoSMILES))
+    morgs = smiles_to_fingerprint(cansmiles)
+
+    ids = pd.DataFrame(tab[['improve_drug_id','canSMILES']]).drop_duplicates()
+    id_morg = ids.rename({"canSMILES":'smile'},axis=1).merge(morgs)[['improve_drug_id','structural_descriptor','descriptor_value']]
+
+    mords = smiles_to_mordred(cansmiles,nproc=ncors)
+
+    id_mord = ids.rename({'canSMILES':'smile'},axis=1).merge(mords)[['improve_drug_id','structural_descriptor','descriptor_value']]
+
+    full = pd.concat([id_morg,id_mord],axis=0)                     
+    full.to_csv(args.outtable,sep='\t',index=False,compression='gzip')
+
+if __name__=='__main__':
+    main()