def calc(smi, name): m = Chem.MolFromSmiles(smi) if m is not None: try: hba = rdMolDescriptors.CalcNumHBA(m) hbd = rdMolDescriptors.CalcNumHBD(m) nrings = rdMolDescriptors.CalcNumRings(m) rtb = rdMolDescriptors.CalcNumRotatableBonds(m) psa = rdMolDescriptors.CalcTPSA(m) logp, mr = rdMolDescriptors.CalcCrippenDescriptors(m) mw = rdMolDescriptors._CalcMolWt(m) csp3 = rdMolDescriptors.CalcFractionCSP3(m) hac = m.GetNumHeavyAtoms() if hac == 0: fmf = 0 else: fmf = GetScaffoldForMol(m).GetNumHeavyAtoms() / hac qed = QED.qed(m) nrings_fused = fused_ring_count(m) n_unique_hba_hbd_atoms = count_hbd_hba_atoms(m) max_ring_size = len(max(m.GetRingInfo().AtomRings(), key=len, default=())) n_chiral_centers = len(FindMolChiralCenters(m, includeUnassigned=True)) fcsp3_bm = rdMolDescriptors.CalcFractionCSP3(GetScaffoldForMol(m)) return name, hba, hbd, hba + hbd, nrings, rtb, round(psa, 2), round(logp, 2), round(mr, 2), round(mw, 2), \ round(csp3, 3), round(fmf, 3), round(qed, 3), hac, nrings_fused, n_unique_hba_hbd_atoms, \ max_ring_size, n_chiral_centers, round(fcsp3_bm, 3) except: sys.stderr.write(f'molecule {name} was omitted due to an error in calculation of some descriptors\n') return None else: sys.stderr.write('smiles %s cannot be parsed (%s)' % (smi, name)) return None
def computeFeatures(mol): numRings = rdMolDescriptors.CalcNumRings(mol) numRotBonds = rdMolDescriptors.CalcNumRotatableBonds(mol) nitrogenCount = countNitrogens(mol) oxygenCount = countOxygens(mol) carbonCount = countCarbons(mol) boronCount = countBorons(mol) phosCount = countPhos(mol) sulfurCount = countSulfurs(mol) fluorCount = countFluorine(mol) iodCount = countIodine(mol) doubleBonds = countDoubleBonds(mol) surf_area = rdMolDescriptors.CalcLabuteASA(mol) mol_weight = rdMolDescriptors.CalcExactMolWt(mol) s_logp = rdMolDescriptors.SlogP_VSA_(mol) dist_hs = recurseMolHCount(mol) output = [ numRings, nitrogenCount, oxygenCount, carbonCount, boronCount, phosCount, sulfurCount, fluorCount, iodCount, doubleBonds, surf_area, mol_weight ] for s in s_logp: output.append(s) for d in dist_hs: output.append(dist_hs[d]) return output
def calc(smi, name): m = Chem.MolFromSmiles(smi) if m is not None: try: hba = rdMolDescriptors.CalcNumHBA(m) hbd = rdMolDescriptors.CalcNumHBD(m) nrings = rdMolDescriptors.CalcNumRings(m) rtb = rdMolDescriptors.CalcNumRotatableBonds(m) psa = rdMolDescriptors.CalcTPSA(m) logp, mr = rdMolDescriptors.CalcCrippenDescriptors(m) mw = rdMolDescriptors._CalcMolWt(m) csp3 = rdMolDescriptors.CalcFractionCSP3(m) hac = m.GetNumHeavyAtoms() if hac == 0: fmf = 0 else: fmf = GetScaffoldForMol(m).GetNumHeavyAtoms() / hac qed = QED.qed(m) nrings_fused = fused_ring_count(m) return name, hba, hbd, hba + hbd, nrings, rtb, round(psa, 2), round(logp, 2), round(mr, 2), round(mw, 2), \ round(csp3, 3), round(fmf, 3), round(qed, 3), hac, nrings_fused except: sys.stderr.write( f'molecule {name} was omitted due to an error in calculation of some descriptors\n' ) return None else: sys.stderr.write('smiles %s cannot be parsed (%s)' % (smi, name)) return None
def _calculateDescriptors(mol): df = pd.DataFrame(index=[0]) df["SlogP"] = rdMolDescriptors.CalcCrippenDescriptors(mol)[0] df["SMR"] = rdMolDescriptors.CalcCrippenDescriptors(mol)[1] df["LabuteASA"] = rdMolDescriptors.CalcLabuteASA(mol) df["TPSA"] = Descriptors.TPSA(mol) df["AMW"] = Descriptors.MolWt(mol) df["ExactMW"] = rdMolDescriptors.CalcExactMolWt(mol) df["NumLipinskiHBA"] = rdMolDescriptors.CalcNumLipinskiHBA(mol) df["NumLipinskiHBD"] = rdMolDescriptors.CalcNumLipinskiHBD(mol) df["NumRotatableBonds"] = rdMolDescriptors.CalcNumRotatableBonds(mol) df["NumHBD"] = rdMolDescriptors.CalcNumHBD(mol) df["NumHBA"] = rdMolDescriptors.CalcNumHBA(mol) df["NumAmideBonds"] = rdMolDescriptors.CalcNumAmideBonds(mol) df["NumHeteroAtoms"] = rdMolDescriptors.CalcNumHeteroatoms(mol) df["NumHeavyAtoms"] = Chem.rdchem.Mol.GetNumHeavyAtoms(mol) df["NumAtoms"] = Chem.rdchem.Mol.GetNumAtoms(mol) df["NumRings"] = rdMolDescriptors.CalcNumRings(mol) df["NumAromaticRings"] = rdMolDescriptors.CalcNumAromaticRings(mol) df["NumSaturatedRings"] = rdMolDescriptors.CalcNumSaturatedRings(mol) df["NumAliphaticRings"] = rdMolDescriptors.CalcNumAliphaticRings(mol) df["NumAromaticHeterocycles"] = \ rdMolDescriptors.CalcNumAromaticHeterocycles(mol) df["NumSaturatedHeterocycles"] = \ rdMolDescriptors.CalcNumSaturatedHeterocycles(mol) df["NumAliphaticHeterocycles"] = \ rdMolDescriptors.CalcNumAliphaticHeterocycles(mol) df["NumAromaticCarbocycles"] = \ rdMolDescriptors.CalcNumAromaticCarbocycles(mol) df["NumSaturatedCarbocycles"] = \ rdMolDescriptors.CalcNumSaturatedCarbocycles(mol) df["NumAliphaticCarbocycles"] = \ rdMolDescriptors.CalcNumAliphaticCarbocycles(mol) df["FractionCSP3"] = rdMolDescriptors.CalcFractionCSP3(mol) df["Chi0v"] = rdMolDescriptors.CalcChi0v(mol) df["Chi1v"] = rdMolDescriptors.CalcChi1v(mol) df["Chi2v"] = rdMolDescriptors.CalcChi2v(mol) df["Chi3v"] = rdMolDescriptors.CalcChi3v(mol) df["Chi4v"] = rdMolDescriptors.CalcChi4v(mol) df["Chi1n"] = rdMolDescriptors.CalcChi1n(mol) df["Chi2n"] = rdMolDescriptors.CalcChi2n(mol) df["Chi3n"] = rdMolDescriptors.CalcChi3n(mol) df["Chi4n"] = rdMolDescriptors.CalcChi4n(mol) df["HallKierAlpha"] = rdMolDescriptors.CalcHallKierAlpha(mol) df["kappa1"] = rdMolDescriptors.CalcKappa1(mol) df["kappa2"] = rdMolDescriptors.CalcKappa2(mol) df["kappa3"] = rdMolDescriptors.CalcKappa3(mol) slogp_VSA = list(map(lambda i: "slogp_VSA" + str(i), list(range(1, 13)))) df = df.assign(**dict(zip(slogp_VSA, rdMolDescriptors.SlogP_VSA_(mol)))) smr_VSA = list(map(lambda i: "smr_VSA" + str(i), list(range(1, 11)))) df = df.assign(**dict(zip(smr_VSA, rdMolDescriptors.SMR_VSA_(mol)))) peoe_VSA = list(map(lambda i: "peoe_VSA" + str(i), list(range(1, 15)))) df = df.assign(**dict(zip(peoe_VSA, rdMolDescriptors.PEOE_VSA_(mol)))) MQNs = list(map(lambda i: "MQN" + str(i), list(range(1, 43)))) df = df.assign(**dict(zip(MQNs, rdMolDescriptors.MQNs_(mol)))) return df
def reward_target_num_rings(mol, target): """ Reward for a target number of rings :param mol: rdkit mol object :param target: int :return: float (-inf, 1] """ x = rdMolDescriptors.CalcNumRings(mol) reward = -1 * (x - target)**2 + 1 return reward
def __init__(self, configuration: StatsExtractionConfig): self._filters = FilterTypesEnum self._columns = DataframeColumnsEnum self._stats = StatsExtractionEnum self._purging = PurgingEnum self._configuration = configuration standardisation_config_dict = self._configuration.standardisation_config standardisation_config = [ FilterConfiguration(name=name, parameters=params) for name, params in standardisation_config_dict.items() ] dec_separator = self._stats.DECORATION_SEPARATOR_TOKEN attachment_token = self._stats.ATTACHMENT_POINT_TOKEN self._mol_wts_udf = psf.udf( lambda x: ExactMolWt(Chem.MolFromSmiles(x)), pst.FloatType()) self._num_rings_udf = psf.udf( lambda x: rdMolDescriptors.CalcNumRings(Chem.MolFromSmiles(x)), pst.IntegerType()) self._num_atoms_udf = psf.udf( lambda x: Chem.MolFromSmiles(x).GetNumHeavyAtoms(), pst.IntegerType()) self._num_aromatic_rings_udf = psf.udf( lambda x: rdMolDescriptors.CalcNumAromaticRings( Chem.MolFromSmiles(x)), pst.IntegerType()) self._hbond_donors_udf = psf.udf( lambda x: rdMolDescriptors.CalcNumHBD(Chem.MolFromSmiles(x)), pst.IntegerType()) self._hbond_acceptors_udf = psf.udf( lambda x: rdMolDescriptors.CalcNumHBA(Chem.MolFromSmiles(x)), pst.IntegerType()) self._hetero_atom_ratio_udf = psf.udf( lambda x: len([ atom for atom in Chem.MolFromSmiles(x).GetAtoms() if atom.GetAtomicNum() == 6 ]) / Chem.MolFromSmiles(x).GetNumHeavyAtoms(), pst.FloatType()) self._make_canonical_udf = psf.udf( lambda x: Chem.MolToSmiles(Chem.MolFromSmiles(x)), pst.StringType()) self._standardise_smiles_udf = psf.udf( lambda x: RDKitStandardizer(standardisation_config, None). apply_filter(x), pst.StringType()) pattern = self._stats.REGEX_TOKENS self.regex = re.compile(pattern) self._tokeniser_udf = psf.udf(self.regex.findall, pst.ArrayType(pst.StringType())) self._decoration_split_udf = psf.udf(lambda x: x.split(dec_separator), pst.ArrayType(pst.StringType())) self._count_decorations_udf = psf.udf( lambda s: list(s).count(attachment_token), pst.IntegerType())
def calc(smi, name): m = Chem.MolFromSmiles(smi) if m is not None: hba = rdMolDescriptors.CalcNumHBA(m) hbd = rdMolDescriptors.CalcNumHBD(m) nrings = rdMolDescriptors.CalcNumRings(m) rtb = rdMolDescriptors.CalcNumRotatableBonds(m) psa = rdMolDescriptors.CalcTPSA(m) logp, mr = rdMolDescriptors.CalcCrippenDescriptors(m) mw = rdMolDescriptors._CalcMolWt(m) csp3 = rdMolDescriptors.CalcFractionCSP3(m) fmf = GetScaffoldForMol(m).GetNumAtoms(onlyHeavy=True) / m.GetNumAtoms(onlyHeavy=True) return name, hba, hbd, hba + hbd, nrings, rtb, round(psa, 2), round(logp, 2), round(mr, 2), round(mw, 2), \ round(csp3, 3), round(fmf, 3) else: sys.stderr.write('smiles %s cannot be parsed (%s)' % (smi, name)) return None
def feature_fp(smiles): mol = Chem.MolFromSmiles(smiles) fp = rdMolDescriptors.MQNs_(mol) fp.append(rdMolDescriptors.CalcNumRotatableBonds(mol)) fp.append(rdMolDescriptors.CalcExactMolWt(mol)) fp.append(rdMolDescriptors.CalcNumRotatableBonds(mol)) fp.append(rdMolDescriptors.CalcFractionCSP3(mol)) fp.append(rdMolDescriptors.CalcNumAliphaticCarbocycles(mol)) fp.append(rdMolDescriptors.CalcNumAliphaticHeterocycles(mol)) fp.append(rdMolDescriptors.CalcNumAliphaticRings((mol))) fp.append(rdMolDescriptors.CalcNumAromaticCarbocycles(mol)) fp.append(rdMolDescriptors.CalcNumAromaticHeterocycles(mol)) fp.append(rdMolDescriptors.CalcNumAromaticRings(mol)) fp.append(rdMolDescriptors.CalcNumBridgeheadAtoms(mol)) fp.append(rdMolDescriptors.CalcNumRings(mol)) fp.append(rdMolDescriptors.CalcNumAmideBonds(mol)) fp.append(rdMolDescriptors.CalcNumHeterocycles(mol)) fp.append(rdMolDescriptors.CalcNumSpiroAtoms(mol)) fp.append(rdMolDescriptors.CalcTPSA(mol)) return np.array(fp)
def get_global_features(self, mol): u = [] # Now get some specific features fdefName = os.path.join(RDConfig.RDDataDir, 'BaseFeatures.fdef') factory = ChemicalFeatures.BuildFeatureFactory(fdefName) feats = factory.GetFeaturesForMol(mol) # First get some basic features natoms = mol.GetNumAtoms() nbonds = mol.GetNumBonds() mw = Descriptors.ExactMolWt(mol) HeavyAtomMolWt = Descriptors.HeavyAtomMolWt(mol) NumValenceElectrons = Descriptors.NumValenceElectrons(mol) ''' # These four descriptors are producing the value of infinity for refcode_csd = YOLJUF (CCOP(=O)(Cc1ccc(cc1)NC(=S)NP(OC(C)C)(OC(C)C)[S])OCC\t\n) MaxAbsPartialCharge = Descriptors.MaxAbsPartialCharge(mol) MaxPartialCharge = Descriptors.MaxPartialCharge(mol) MinAbsPartialCharge = Descriptors.MinAbsPartialCharge(mol) MinPartialCharge = Descriptors.MinPartialCharge(mol) ''' # FpDensityMorgan1 = Descriptors.FpDensityMorgan1(mol) # FpDensityMorgan2 = Descriptors.FpDensityMorgan2(mol) # FpDensityMorgan3 = Descriptors.FpDensityMorgan3(mol) # Get some features using chemical feature factory nbrAcceptor = 0 nbrDonor = 0 nbrHydrophobe = 0 nbrLumpedHydrophobe = 0 nbrPosIonizable = 0 nbrNegIonizable = 0 for j in range(len(feats)): #print(feats[j].GetFamily(), feats[j].GetType()) if ('Acceptor' == (feats[j].GetFamily())): nbrAcceptor = nbrAcceptor + 1 elif ('Donor' == (feats[j].GetFamily())): nbrDonor = nbrDonor + 1 elif ('Hydrophobe' == (feats[j].GetFamily())): nbrHydrophobe = nbrHydrophobe + 1 elif ('LumpedHydrophobe' == (feats[j].GetFamily())): nbrLumpedHydrophobe = nbrLumpedHydrophobe + 1 elif ('PosIonizable' == (feats[j].GetFamily())): nbrPosIonizable = nbrPosIonizable + 1 elif ('NegIonizable' == (feats[j].GetFamily())): nbrNegIonizable = nbrNegIonizable + 1 else: pass #print(feats[j].GetFamily()) # Now get some features using rdMolDescriptors moreGlobalFeatures = [rdm.CalcNumRotatableBonds(mol), rdm.CalcChi0n(mol), rdm.CalcChi0v(mol), \ rdm.CalcChi1n(mol), rdm.CalcChi1v(mol), rdm.CalcChi2n(mol), rdm.CalcChi2v(mol), \ rdm.CalcChi3n(mol), rdm.CalcChi4n(mol), rdm.CalcChi4v(mol), \ rdm.CalcFractionCSP3(mol), rdm.CalcHallKierAlpha(mol), rdm.CalcKappa1(mol), \ rdm.CalcKappa2(mol), rdm.CalcLabuteASA(mol), \ rdm.CalcNumAliphaticCarbocycles(mol), rdm.CalcNumAliphaticHeterocycles(mol), \ rdm.CalcNumAliphaticRings(mol), rdm.CalcNumAmideBonds(mol), \ rdm.CalcNumAromaticCarbocycles(mol), rdm.CalcNumAromaticHeterocycles(mol), \ rdm.CalcNumAromaticRings(mol), rdm.CalcNumBridgeheadAtoms(mol), rdm.CalcNumHBA(mol), \ rdm.CalcNumHBD(mol), rdm.CalcNumHeteroatoms(mol), rdm.CalcNumHeterocycles(mol), \ rdm.CalcNumLipinskiHBA(mol), rdm.CalcNumLipinskiHBD(mol), rdm.CalcNumRings(mol), \ rdm.CalcNumSaturatedCarbocycles(mol), rdm.CalcNumSaturatedHeterocycles(mol), \ rdm.CalcNumSaturatedRings(mol), rdm.CalcNumSpiroAtoms(mol), rdm.CalcTPSA(mol)] u = [natoms, nbonds, mw, HeavyAtomMolWt, NumValenceElectrons, \ nbrAcceptor, nbrDonor, nbrHydrophobe, nbrLumpedHydrophobe, \ nbrPosIonizable, nbrNegIonizable] u = u + moreGlobalFeatures u = np.array(u).T # Some of the descriptors produice NAN. We can convert them to 0 # If you are getting outliers in the training or validation set this could be # Because some important features were set to zero here because it produced NAN # Removing those features from the feature set might remove the outliers #u[np.isnan(u)] = 0 #u = torch.tensor(u, dtype=torch.float) return (u)
NumHeteroatoms.version = "1.0.0" _Heteroatoms = lambda x, y=HeteroatomSmarts: x.GetSubstructMatches(y, uniquify=1) NumRotatableBonds = lambda x: rdMolDescriptors.CalcNumRotatableBonds(x) NumRotatableBonds.__doc__ = "Number of Rotatable Bonds" NumRotatableBonds.version = "1.0.0" _RotatableBonds = lambda x, y=RotatableBondSmarts: x.GetSubstructMatches( y, uniquify=1) NOCount = lambda x: rdMolDescriptors.CalcNumLipinskiHBA(x) NOCount.__doc__ = "Number of Nitrogens and Oxygens" NOCount.version = "1.0.0" NHOHCount = lambda x: rdMolDescriptors.CalcNumLipinskiHBD(x) NHOHCount.__doc__ = "Number of NHs or OHs" NHOHCount.version = "2.0.0" RingCount = lambda x: rdMolDescriptors.CalcNumRings(x) RingCount.version = "1.0.0" def HeavyAtomCount(mol): " Number of heavy atoms a molecule." return mol.GetNumHeavyAtoms() HeavyAtomCount.version = "1.0.1" _bulkConvert = ("CalcFractionCSP3", "CalcNumAromaticRings", "CalcNumSaturatedRings", "CalcNumAromaticHeterocycles", "CalcNumAromaticCarbocycles", "CalcNumSaturatedHeterocycles", "CalcNumSaturatedCarbocycles", "CalcNumAliphaticRings", "CalcNumAliphaticHeterocycles", "CalcNumAliphaticCarbocycles")
return fig #FRAGMENTS = { # "acyl_halide": Chem.MolFromSmarts('[#9,#17,#35,#53]=O'), # C(=O)X # "anhydride": Chem.MolFromSmarts('[#6]-[#6](=O)-[#8]-[#6](-[#6])=O'), # CC(=O)OC(=O)C # "peroxide": Chem.MolFromSmarts('[#8]-[#8]'), # R-O-O-R' # "ab_unsaturated_ketone": Chem.MolFromSmarts('[#6]=[#6]-[#6]=O'), # R=CC=O #} DESCRIPTORS = { # classical molecular descriptors "num_heavy_atoms": lambda x: x.GetNumAtoms(), "molecular_weight": lambda x: round(Desc.ExactMolWt(x), 4), "num_rings": lambda x: rdMolDesc.CalcNumRings(x), "num_rings_arom": lambda x: rdMolDesc.CalcNumAromaticRings(x), "num_rings_ali": lambda x: rdMolDesc.CalcNumAliphaticRings(x), "num_hbd": lambda x: rdMolDesc.CalcNumLipinskiHBD(x), "num_hba": lambda x: rdMolDesc.CalcNumLipinskiHBA(x), "slogp": lambda x: round(Crippen.MolLogP(x), 4), "tpsa": lambda x: round(rdMolDesc.CalcTPSA(x), 4), "num_rotatable_bond": lambda x: rdMolDesc.CalcNumRotatableBonds(x), "num_atoms_oxygen": lambda x: len( [a for a in x.GetAtoms() if a.GetAtomicNum() == 8] ), "num_atoms_nitrogen": lambda x: len( [a for a in x.GetAtoms() if a.GetAtomicNum() == 7] ), "num_atoms_halogen": Fragments.fr_halogen, "num_atoms_bridgehead": rdMolDesc.CalcNumBridgeheadAtoms,
def datadump(database, dumpdir): db = pickle.load(open(database, "rb")) if os.path.exists(dumpdir): raise Warning( "Caution, %s already exists. Already existing data may be overwritten." ) else: os.mkdir(dumpdir) os.mkdir(dumpdir + "/png") frag2mol = db.get_frag2mol() frag2lcapconn = db.get_frag2lcapconn() frag2rcapconn = db.get_frag2rcapconn() mol2frag = db.get_mol2frag() mol2conn = db.get_mol2conn() frag_log = logger(dumpdir + "/frag.dat") frag_log.log("### datadump of database %s" % database) frag_log.log("### timestamp %s" % time.asctime(time.localtime(time.time()))) frag_log.log("### written by run_fragresp.py datadump routine.") frag_log.log("###") frag_log.log("### ----------------- ###") frag_log.log("### FRAGMENT DATA LOG ###") frag_log.log("### ----------------- ###") frag_log.log("###") frag_log.log( "# id smiles mol_id lcap_id rcap_id Natoms Nbonds Nnonhatoms Chg Nhbd Nhba Nrotbonds Nrings" ) for frag_i in range(db.get_frag_count()): frag = db.get_frag(frag_i) Chem.SanitizeMol(frag) log_str = list() ### id log_str.append(str(frag_i) + " ") ### smiles log_str.append(str(Chem.MolToSmiles(frag, isomericSmiles=True)) + " ") ### mol_id mol_count = len(frag2mol[frag_i]) if mol_count == 0: log_str.append("-1 ") else: for i in range(mol_count): mol_i = frag2mol[frag_i][i] if i < mol_count - 1: log_str.append(str(mol_i) + ",") else: log_str.append(str(mol_i) + " ") ### lcap_id lcap_count = len(frag2lcapconn[frag_i]) if lcap_count == 0: log_str.append("-1 ") else: for i in range(lcap_count): cap_i = frag2lcapconn[frag_i][i] if i < lcap_count - 1: log_str.append(str(cap_i) + ",") else: log_str.append(str(cap_i) + " ") ### rcap_id rcap_count = len(frag2rcapconn[frag_i]) if rcap_count == 0: log_str.append("-1 ") else: for i in range(rcap_count): cap_i = frag2rcapconn[frag_i][i] if i < rcap_count - 1: log_str.append(str(cap_i) + ",") else: log_str.append(str(cap_i) + " ") ### N_atoms log_str.append(str(frag.GetNumAtoms()) + " ") ### N_bonds log_str.append(str(frag.GetNumBonds()) + " ") ### Nnonhatoms log_str.append(str(frag.GetNumHeavyAtoms()) + " ") ### Chg log_str.append(str(rdmolops.GetFormalCharge(frag)) + " ") ### Nhbd log_str.append(str(rdMolDescriptors.CalcNumHBD(frag)) + " ") ### Nhba log_str.append(str(rdMolDescriptors.CalcNumHBA(frag)) + " ") ### Nrotbonds log_str.append(str(rdMolDescriptors.CalcNumRotatableBonds(frag)) + " ") ### Nrings log_str.append(str(rdMolDescriptors.CalcNumRings(frag)) + " ") frag_log.log("".join(log_str)) png_path = dumpdir + "/png/" + "frag_%d.png" % frag_i try: Chem.SanitizeMol(frag) AllChem.Compute2DCoords(frag) Draw.MolToFile(frag, png_path, size=(500, 500)) except: #Chem.Kekulize(frag) print("Could not save frag %d to disk." % frag_i) frag_log.close() mol_log = logger(dumpdir + "/mol.dat") mol_log.log("### datadump of database %s" % database) mol_log.log("### timestamp %s" % time.asctime(time.localtime(time.time()))) mol_log.log("### written by run_fragresp.py datadump routine.") mol_log.log("###") mol_log.log("### ----------------- ###") mol_log.log("### MOLECULE DATA LOG ###") mol_log.log("### ----------------- ###") mol_log.log("###") mol_log.log( "# id name smiles frag_id Natoms Nbonds Nnonhatoms Chg Nhbd Nhba Nrotbonds Nrings" ) for mol_i in range(db.get_mol_count()): mol = db.get_mol(mol_i) Chem.SanitizeMol(mol) name = db.get_name(mol_i) decomp = db.get_decompose(mol_i) log_str = list() log_str.append(str(mol_i) + " ") log_str.append(name + " ") log_str.append(str(Chem.MolToSmiles(mol, isomericSmiles=True)) + " ") frag_count = decomp.get_frag_count() if frag_count == 0: log_str.append("-1 ") else: for i in range(frag_count): frag_i = mol2frag[mol_i][i] if i < frag_count - 1: log_str.append(str(frag_i) + ",") else: log_str.append(str(frag_i) + " ") log_str.append(str(mol.GetNumAtoms()) + " ") log_str.append(str(mol.GetNumBonds()) + " ") log_str.append(str(mol.GetNumHeavyAtoms()) + " ") log_str.append(str(rdmolops.GetFormalCharge(mol)) + " ") log_str.append(str(rdMolDescriptors.CalcNumHBD(mol)) + " ") log_str.append(str(rdMolDescriptors.CalcNumHBA(mol)) + " ") log_str.append(str(rdMolDescriptors.CalcNumRotatableBonds(mol)) + " ") log_str.append(str(rdMolDescriptors.CalcNumRings(mol)) + " ") mol_log.log("".join(log_str)) png_path = dumpdir + "/png/" + "mol_%d.png" % mol_i AllChem.Compute2DCoords(mol) Chem.Kekulize(mol) Draw.MolToFile(mol, png_path, size=(500, 500)) mol_log.close() surr_log = logger(dumpdir + "/surr.dat") surr_log.log("### datadump of database %s" % database) surr_log.log("### timestamp %s" % time.asctime(time.localtime(time.time()))) surr_log.log("### written by run_fragresp.py datadump routine.") surr_log.log("###") surr_log.log("### ----------------- ###") surr_log.log("### SURROGATE DATA LOG ###") surr_log.log("### ------------------ ###") surr_log.log("###") surr_log.log( "# id name smiles mol_id Natoms Nbonds Nnonhatoms Chg Nhbd Nhba Nrotbonds Nrings" ) for conn_i, conn in enumerate(db.get_conn_list()): if conn.get_terminal(): continue name = conn.get_name() conn_cap = conn.get_surrogate_cap() Chem.SanitizeMol(conn_cap) log_str = list() log_str.append(str(conn_i) + " ") log_str.append(name + " ") log_str.append( str(Chem.MolToSmiles(conn_cap, isomericSmiles=True)) + " ") conn2mol = db.get_conn2mol()[conn_i] mol_count = len(conn2mol) if mol_count == 0: log_str.append("-1 ") else: for i in range(mol_count): mol_i = conn2mol[i] if i < mol_count - 1: log_str.append(str(mol_i) + ",") else: log_str.append(str(mol_i) + " ") log_str.append(str(conn_cap.GetNumAtoms()) + " ") log_str.append(str(conn_cap.GetNumBonds()) + " ") log_str.append(str(conn_cap.GetNumHeavyAtoms()) + " ") log_str.append(str(rdmolops.GetFormalCharge(conn_cap)) + " ") log_str.append(str(rdMolDescriptors.CalcNumHBD(conn_cap)) + " ") log_str.append(str(rdMolDescriptors.CalcNumHBA(conn_cap)) + " ") log_str.append( str(rdMolDescriptors.CalcNumRotatableBonds(conn_cap)) + " ") log_str.append(str(rdMolDescriptors.CalcNumRings(conn_cap)) + " ") surr_log.log("".join(log_str)) png_path = dumpdir + "/png/" + "surr_%s.png" % (conn_i) AllChem.Compute2DCoords(conn_cap) Chem.Kekulize(conn_cap) Draw.MolToFile(conn_cap, png_path, size=(500, 500)) surr_log.close()
def num_rings(mol: Mol) -> int: return rdMolDescriptors.CalcNumRings(mol)
def calculate_number_rings(self): ''' Number of rings in the molecule :return: ''' return rdMolDescriptors.CalcNumRings(self.mol)
def get_molecular_features(dataframe, mol_list): df = dataframe for i in range(len(mol_list)): print("Getting molecular features for molecule: ", i) mol = mol_list[i] natoms = mol.GetNumAtoms() nbonds = mol.GetNumBonds() mw = Descriptors.ExactMolWt(mol) df.at[i,"NbrAtoms"] = natoms df.at[i,"NbrBonds"] = nbonds df.at[i,"mw"] = mw df.at[i,'HeavyAtomMolWt'] = Chem.Descriptors.HeavyAtomMolWt(mol) df.at[i,'NumValenceElectrons'] = Chem.Descriptors.NumValenceElectrons(mol) ''' # These four descriptors are producing the value of infinity for refcode_csd = YOLJUF (CCOP(=O)(Cc1ccc(cc1)NC(=S)NP(OC(C)C)(OC(C)C)[S])OCC\t\n) df.at[i,'MaxAbsPartialCharge'] = Chem.Descriptors.MaxAbsPartialCharge(mol) df.at[i,'MaxPartialCharge'] = Chem.Descriptors.MaxPartialCharge(mol) df.at[i,'MinAbsPartialCharge'] = Chem.Descriptors.MinAbsPartialCharge(mol) df.at[i,'MinPartialCharge'] = Chem.Descriptors.MinPartialCharge(mol) ''' df.at[i,'FpDensityMorgan1'] = Chem.Descriptors.FpDensityMorgan1(mol) df.at[i,'FpDensityMorgan2'] = Chem.Descriptors.FpDensityMorgan2(mol) df.at[i,'FpDensityMorgan3'] = Chem.Descriptors.FpDensityMorgan3(mol) #print(natoms, nbonds) # Now get some specific features fdefName = os.path.join(RDConfig.RDDataDir,'BaseFeatures.fdef') factory = ChemicalFeatures.BuildFeatureFactory(fdefName) feats = factory.GetFeaturesForMol(mol) #df["Acceptor"] = 0 #df["Aromatic"] = 0 #df["Hydrophobe"] = 0 nbrAcceptor = 0 nbrDonor = 0 nbrHydrophobe = 0 nbrLumpedHydrophobe = 0 nbrPosIonizable = 0 nbrNegIonizable = 0 for j in range(len(feats)): #print(feats[j].GetFamily(), feats[j].GetType()) if ('Acceptor' == (feats[j].GetFamily())): nbrAcceptor = nbrAcceptor + 1 elif ('Donor' == (feats[j].GetFamily())): nbrDonor = nbrDonor + 1 elif ('Hydrophobe' == (feats[j].GetFamily())): nbrHydrophobe = nbrHydrophobe + 1 elif ('LumpedHydrophobe' == (feats[j].GetFamily())): nbrLumpedHydrophobe = nbrLumpedHydrophobe + 1 elif ('PosIonizable' == (feats[j].GetFamily())): nbrPosIonizable = nbrPosIonizable + 1 elif ('NegIonizable' == (feats[j].GetFamily())): nbrNegIonizable = nbrNegIonizable + 1 else: pass#print(feats[j].GetFamily()) df.at[i,"Acceptor"] = nbrAcceptor df.at[i,"Donor"] = nbrDonor df.at[i,"Hydrophobe"] = nbrHydrophobe df.at[i,"LumpedHydrophobe"] = nbrLumpedHydrophobe df.at[i,"PosIonizable"] = nbrPosIonizable df.at[i,"NegIonizable"] = nbrNegIonizable # We can also get some more molecular features using rdMolDescriptors df.at[i,"NumRotatableBonds"] = rdMolDescriptors.CalcNumRotatableBonds(mol) df.at[i,"CalcChi0n"] = rdMolDescriptors.CalcChi0n(mol) df.at[i,"CalcChi0v"] = rdMolDescriptors.CalcChi0v(mol) df.at[i,"CalcChi1n"] = rdMolDescriptors.CalcChi1n(mol) df.at[i,"CalcChi1v"] = rdMolDescriptors.CalcChi1v(mol) df.at[i,"CalcChi2n"] = rdMolDescriptors.CalcChi2n(mol) df.at[i,"CalcChi2v"] = rdMolDescriptors.CalcChi2v(mol) df.at[i,"CalcChi3n"] = rdMolDescriptors.CalcChi3n(mol) df.at[i,"CalcChi3v"] = rdMolDescriptors.CalcChi3v(mol) df.at[i,"CalcChi4n"] = rdMolDescriptors.CalcChi4n(mol) df.at[i,"CalcChi4v"] = rdMolDescriptors.CalcChi4v(mol) df.at[i,"CalcFractionCSP3"] = rdMolDescriptors.CalcFractionCSP3(mol) df.at[i,"CalcHallKierAlpha"] = rdMolDescriptors.CalcHallKierAlpha(mol) df.at[i,"CalcKappa1"] = rdMolDescriptors.CalcKappa1(mol) df.at[i,"CalcKappa2"] = rdMolDescriptors.CalcKappa2(mol) #df.at[i,"CalcKappa3"] = rdMolDescriptors.CalcKappa3(mol) df.at[i,"CalcLabuteASA"] = rdMolDescriptors.CalcLabuteASA(mol) df.at[i,"CalcNumAliphaticCarbocycles"] = rdMolDescriptors.CalcNumAliphaticCarbocycles(mol) df.at[i,"CalcNumAliphaticHeterocycles"] = rdMolDescriptors.CalcNumAliphaticHeterocycles(mol) df.at[i,"CalcNumAliphaticRings"] = rdMolDescriptors.CalcNumAliphaticRings(mol) df.at[i,"CalcNumAmideBonds"] = rdMolDescriptors.CalcNumAmideBonds(mol) df.at[i,"CalcNumAromaticCarbocycles"] = rdMolDescriptors.CalcNumAromaticCarbocycles(mol) df.at[i,"CalcNumAromaticHeterocycles"] = rdMolDescriptors.CalcNumAromaticHeterocycles(mol) df.at[i,"CalcNumAromaticRings"] = rdMolDescriptors.CalcNumAromaticRings(mol) df.at[i,"CalcNumBridgeheadAtoms"] = rdMolDescriptors.CalcNumBridgeheadAtoms(mol) df.at[i,"CalcNumHBA"] = rdMolDescriptors.CalcNumHBA(mol) df.at[i,"CalcNumHBD"] = rdMolDescriptors.CalcNumHBD(mol) df.at[i,"CalcNumHeteroatoms"] = rdMolDescriptors.CalcNumHeteroatoms(mol) df.at[i,"CalcNumHeterocycles"] = rdMolDescriptors.CalcNumHeterocycles(mol) df.at[i,"CalcNumLipinskiHBA"] = rdMolDescriptors.CalcNumLipinskiHBA(mol) df.at[i,"CalcNumLipinskiHBD"] = rdMolDescriptors.CalcNumLipinskiHBD(mol) df.at[i,"CalcNumRings"] = rdMolDescriptors.CalcNumRings(mol) df.at[i,"CalcNumSaturatedCarbocycles"] = rdMolDescriptors.CalcNumSaturatedCarbocycles(mol) df.at[i,"CalcNumSaturatedHeterocycles"] = rdMolDescriptors.CalcNumSaturatedHeterocycles(mol) df.at[i,"CalcNumSaturatedRings"] = rdMolDescriptors.CalcNumSaturatedRings(mol) df.at[i,"CalcNumSpiroAtoms"] = rdMolDescriptors.CalcNumSpiroAtoms(mol) df.at[i,"CalcTPSA"] = rdMolDescriptors.CalcTPSA(mol) return(df)