def compareDistances(actives, decoys):
actives_fps = utilities.getFingerprintList(actives)[0]
decoys_fps = utilities.getFingerprintList(decoys)[0]
distances_min = []
distances_max = []
for decoy in decoys_fps:
dists = utilities.getMolDistFromSet(decoy, actives_fps)
distances_min.append(dists[0])
distances_max.append(dists[1])
return numpy.mean(distances_min), numpy.mean(distances_max)
def predict(classmodel, regressmodel, molfile_path):
print "Starting predictions for: " + molfile_path
suppl = Chem.SDMolSupplier(molfile_path)
mols = dict()
for mol in suppl:
pmol = PropertyMol.PropertyMol(mol)
mols[pmol.GetProp("_Name")] = {"RDKit" : pmol}
fingerprinter.appendMorganFingerprints(mols, dump=None)
actives = pickle.load(open(ACTIVES_DUMP, 'rb'))
found_sth = False
for mol in mols:
prediction = classmodel.predict(mols[mol]['fingerprint'])
fingerprints_actives = utilities.getFingerprintList(actives)[0]
min_distance = utilities.getMolDistFromSet(mols[mol]['fingerprint'], fingerprints_actives)[0]
if min_distance <= APPLICABILITY_DOMAIN_DISTANCE_THRESHOLD and prediction[0]:
print mol + " is active"
print "Predicted pIC50: " + str(regressmodel.predict(mols[mol]['fingerprint'])[0])
found_sth = True
if not found_sth:
print "None of the molecules within the specified set was found to be active."
