def compute_connectivity_and_shape_indexes(self):
"""compute the compute connectivity and shape indexes.
Ref: Rev. Comput. Chem. 2:367-422 (1991)
Returns:
CSI_dict: CSI dictionary, data type: float
"""
assert type(self.Molecule) == Chem.rdchem.Mol
CSI_dict = {}
CSI_dict['Chi0v'] = rdDesc.CalcChi0v(self.Molecule)
CSI_dict['Chi1v'] = rdDesc.CalcChi1v(self.Molecule)
CSI_dict['Chi2v'] = rdDesc.CalcChi2v(self.Molecule)
CSI_dict['Chi3v'] = rdDesc.CalcChi3v(self.Molecule)
CSI_dict['Chi4v'] = rdDesc.CalcChi4v(self.Molecule)
CSI_dict['Chi0n'] = rdDesc.CalcChi0n(self.Molecule)
CSI_dict['Chi1n'] = rdDesc.CalcChi1n(self.Molecule)
CSI_dict['Chi2n'] = rdDesc.CalcChi2n(self.Molecule)
CSI_dict['Chi3n'] = rdDesc.CalcChi3n(self.Molecule)
CSI_dict['Chi4n'] = rdDesc.CalcChi4n(self.Molecule)
CSI_dict['HallKierAlpha'] = rdDesc.CalcHallKierAlpha(self.Molecule)
CSI_dict['Kappa1'] = rdDesc.CalcKappa1(self.Molecule)
CSI_dict['Kappa2'] = rdDesc.CalcKappa2(self.Molecule)
CSI_dict['Kappa3'] = rdDesc.CalcKappa3(self.Molecule)
return CSI_dict
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
denom = (P3 + alpha)**2
if denom:
if A % 2 == 1:
kappa = (A + alpha - 1) * (A + alpha - 3)**2 / denom
else:
kappa = (A + alpha - 2) * (A + alpha - 3)**2 / denom
else:
kappa = 0
return kappa
# Kappa3.version="1.0.0"
HallKierAlpha = lambda x: rdMolDescriptors.CalcHallKierAlpha(x)
HallKierAlpha.version = rdMolDescriptors._CalcHallKierAlpha_version
Kappa1 = lambda x: rdMolDescriptors.CalcKappa1(x)
Kappa1.version = rdMolDescriptors._CalcKappa1_version
Kappa2 = lambda x: rdMolDescriptors.CalcKappa2(x)
Kappa2.version = rdMolDescriptors._CalcKappa2_version
Kappa3 = lambda x: rdMolDescriptors.CalcKappa3(x)
Kappa3.version = rdMolDescriptors._CalcKappa3_version
def Chi0(mol):
""" From equations (1),(9) and (10) of Rev. Comp. Chem. vol 2, 367-422, (1991)
"""
deltas = [x.GetDegree() for x in mol.GetAtoms()]
while 0 in deltas:
deltas.remove(0)
deltas = numpy.array(deltas, 'd')
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)
def get_fingerprint(SMILES=None, E_BIND=None):
"""
PRE: Takes in a MOLECULE as a SMILES
POST: Prints its finger prints as two list, the first contains the names, the second contains the fingerprints
"""
def get_atoms_coords(RDKIT_BLOCK):
"""Takes as input an RDKIT BLOCK and returns a list of atoms with a numpy array containing the coordinates"""
RDKIT_BLOCK = RDKIT_BLOCK.split('\n')
atm_number = int(RDKIT_BLOCK[3][:3])
RDKIT_BLOCK = [x.split() for x in RDKIT_BLOCK]
atm_list = []
coords_array = np.zeros([atm_number, 3], dtype=float)
for i, line in enumerate(RDKIT_BLOCK[4:4 + atm_number]):
coords_atm = line
atm_list.append(coords_atm[3])
coords_array[i, :] = coords_atm[:3]
return atm_list, coords_array
def get_atom_types(mol):
"""
PRE: Takes in the mol
POST: Returns a dictionary with the atom types and numbers
"""
atom_types = {}
for atom in mol.GetAtoms():
symbol = atom.GetSymbol()
if symbol in atom_types:
atom_types[symbol] += 1
else:
atom_types[symbol] = 1
return atom_types
def AreRingFused(mol):
"""
PRE : Takes in a mol rdkit
POST : Returns the max number of fused rings. That is the maximum number of rings any atom belongs to
"""
rings = Chem.GetSymmSSSR(mol)
ring_dic = {}
for ring in rings:
for atom in list(ring):
if atom in ring_dic:
ring_dic[atom] += 1
else:
ring_dic[atom] = 1
if ring_dic.values() == []:
return 0
else:
return max(ring_dic.values())
def getVolume(mol, atom_types):
"""
PRE: Takes in a mol with HYDROGENS ADDED
POST: Returns its volume computed as a linear combination of the contribution of the vdW volumes
"""
index_of_vols = {'H': 7.24, 'C': 20.58, 'N': 15.60, 'O': 14.71, 'F': 13.31, 'Cl': 22.45, 'Br': 26.52,
'I': 32.52, 'P': 24.43, 'S': 24.43, 'As': 26.52, 'B': 40.48, 'Si': 38.79, 'Se': 28.73,
'Te': 36.62}
gross_volume = 0
# for sym in atom_types:
# gross_volume += atom_types[sym] * index_of_vols[sym]
bonds = mol.GetNumBonds()
rings = Chem.GetSymmSSSR(mol)
# print 'aromatic ring count is ',descriptors.CalcNumAromaticRings(mol)
# print 'aliphatic ring count is ',descriptors.CalcNumAliphaticRings(mol)
ra = 0
largest_ra = 0
rna = 0
largest_rna = 0
for ringId in range(len(rings)):
if isRingAromatic(mol, tuple(rings[ringId])):
ra += 1
if largest_ra < len(rings[ringId]):
largest_ra = len(rings[ringId])
else:
rna += 1
if largest_rna < len(rings[ringId]):
largest_rna = len(rings[ringId])
# volume = gross_volume - 5.92 * bonds - 14.7 * ra - 3.8 * rna
try:
AllChem.EmbedMolecule(mol)
AllChem.MMFFOptimizeMolecule(mol)
volume = AllChem.ComputeMolVolume(mol)
except:
raise ValueError("Can't build the molecule")
return volume, ra, rna, largest_ra, largest_rna
def isRingAromatic(mol, ring):
"""
PRE: Takes in a mol and a ring given as a tuple of atom id
POST: Returns TRUE is all the atoms inside the ring are aromatic and FALSE otherwise
"""
aromatic = True
for ids in ring:
if mol.GetAtomWithIdx(ids).GetIsAromatic():
# print ids
pass
else:
aromatic = False
break
return aromatic
mol = SMILES
features = [
'atomNbr',
'Volume',
'NAtom',
'OAtom',
'SAtom',
'PAtom',
'ClAtom',
'BrAtom',
'FAtom',
'IAtom',
'AromaticRingNumber',
'LargestAromaticRingAtomNbr',
'NonAromaticRingNumber',
'LargestNonAromaticRingAtomNbr',
'MaxNbrFusedRings',
'SurfaceArea',
'Charge',
# 'MinRadiusOfCylinder',
# 'RadiusOfCylinderBestConf',
'NitroNbr',
'AlcoholNbr',
'KetoneNbr',
'NitrileNbr',
'ThiolNbr',
'Phenol_likeNbr',
'EsterNbr',
'SulfideNbr',
'CarboxilicAcidNbr',
'EtherNbr',
'AmideNbr',
'AnilineNbr',
'PrimaryAmineNbr',
'SecondaryAmineNbr',
'RotableBondNum',
'HBondDonor',
'HBondAcceptor',
'MolLogP',
'MolMR'
]
for i in range(6):
features.append('Chi{}v'.format(i + 1))
features.append('Chi{}n'.format(i + 1))
if i < 3:
features.append('Kappa{}'.format(i + 1))
feature_dic = dict.fromkeys(features)
if mol == None:
return sorted(feature_dic.keys())
mol = Chem.MolFromSmiles(SMILES)
mol = Chem.AddHs(mol)
feature_dic['RotableBondNum'] = descriptors.CalcNumRotatableBonds(mol)
for i in range(6):
feature_dic['Chi{}v'.format(i + 1)] = descriptors.CalcChiNv(mol, i + 1)
feature_dic['Chi{}n'.format(i + 1)] = descriptors.CalcChiNn(mol, i + 1)
feature_dic['Kappa1'] = descriptors.CalcKappa1(mol)
feature_dic['Kappa2'] = descriptors.CalcKappa2(mol)
feature_dic['Kappa3'] = descriptors.CalcKappa3(mol)
feature_dic['HBondAcceptor'] = descriptors.CalcNumHBA(mol)
feature_dic['HBondDonor'] = descriptors.CalcNumHBD(mol)
CrippenDescriptors = descriptors.CalcCrippenDescriptors(mol)
feature_dic['MolLogP'] = CrippenDescriptors[0]
feature_dic['MolMR'] = CrippenDescriptors[1]
atom_types = get_atom_types(mol)
for feat, symbol in zip(['NAtom', 'OAtom', 'SAtom', 'PAtom', 'ClAtom', 'BrAtom', 'FAtom', 'IAtom'],
['N', 'O', 'S', 'P', 'Cl', 'Br', 'F', 'I']):
if symbol in atom_types:
feature_dic[feat] = atom_types[symbol]
else:
feature_dic[feat] = 0
feature_dic['atomNbr'] = mol.GetNumHeavyAtoms()
feature_dic['Volume'], feature_dic['AromaticRingNumber'], feature_dic['NonAromaticRingNumber'], feature_dic[
'LargestAromaticRingAtomNbr'], feature_dic['LargestNonAromaticRingAtomNbr'] = getVolume(mol, atom_types)
feature_dic['MaxNbrFusedRings'] = AreRingFused(mol)
feature_dic['SurfaceArea'] = descriptors.CalcTPSA(mol)
feature_dic['Charge'] = Chem.GetFormalCharge(mol)
funct_dic = {
'[$([NX3](=O)=O),$([NX3+](=O)[O-])][!#8]': 'NitroNbr',
'[#6][OX2H]': 'AlcoholNbr',
'[NX1]#[CX2]': 'NitrileNbr',
'[#6][CX3](=O)[#6]': 'KetoneNbr',
'[#16X2H]': 'ThiolNbr',
"[OX2H][cX3][c]": 'Phenol_likeNbr',
'[#6][CX3](=O)[OX2H0][#6]': 'EsterNbr',
'[#16X2H0]': 'SulfideNbr',
'[CX3](=O)[OX2H1]': 'CarboxilicAcidNbr',
'[OD2]([#6])[#6]': 'EtherNbr',
# '[NX3][CX3](=[OX1])[#6]':'AmideNbr',
'[#7X3][#6X3](=[OX1])[#6]': 'AmideNbr',
'[NX3][cc]': 'AnilineNbr',
'[NX3H2;!$(NC=O)]': 'PrimaryAmineNbr',
'[NX3H1;!$(NC=O)]': 'SecondaryAmineNbr'}
for funct in funct_dic:
patt = Chem.MolFromSmarts(funct)
feature_dic[funct_dic[funct]] = len(mol.GetSubstructMatches(patt))
# names, coords = get_atoms_coords(Chem.MolToMolBlock(mol))
# feature_dic['MinRadiusOfCylinder'] = returnCircleAsTuple(coords[:,1:])[2]
# feature_dic['MinRadiusOfCylinder'] = RADIUS[0]
# feature_dic['RadiusOfCylinderBestConf'] = RADIUS[1]
values = []
for key in sorted(feature_dic.keys()):
values.append(feature_dic[key])
# print key, feature_dic[key]
return values
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)