def testGithub1973(self):
smiles = ("c1ccccc1S", "c1cscc1", "CC(=S)C", "CSC", "CS(=O)C", "CP(C)C", "CP=O", "CP(C)(C)=O",
"C[PH](C)=O")
orig_tpsa = (0, 0, 0, 0, 17.07, 0.0, 17.07, 17.07, 17.07)
new_tpsa = (38.8, 28.24, 32.09, 25.30, 36.28, 13.59, 51.21, 26.88, 40.54)
for i, smi in enumerate(smiles):
mol = Chem.MolFromSmiles(smi)
oTPSA = rdMD.CalcTPSA(mol)
self.assertAlmostEqual(oTPSA, orig_tpsa[i], 2)
nTPSA = rdMD.CalcTPSA(mol, force=True, includeSandP=True)
self.assertAlmostEqual(nTPSA, new_tpsa[i], 2)
def get_filter_values(mol):
"""
calculate the values, for a given molecule, that are used to filter
return as a dictionary
"""
assert isinstance(mol, Chem.Mol)
values = {}
values["MW"] = desc.CalcExactMolWt(mol)
values["logP"] = crip.MolLogP(mol)
values["HBA"] = lip.NumHAcceptors(mol)
values["HBD"] = lip.NumHDonors(mol)
values["tPSA"] = desc.CalcTPSA(mol)
values["rot_bonds"] = lip.NumRotatableBonds(mol)
values["rigid_bonds"] = mol.GetNumBonds() - values["rot_bonds"] # assume mutual exclusion
values["num_rings"] = lip.RingCount(mol)
values["num_hetero_atoms"] = lip.NumHeteroatoms(mol)
values["charge"] = rdmolops.GetFormalCharge(mol) # trusting this charge calculation method
values["num_carbons"], values["num_charges"], values["max_ring_size"] = get_atom_props(mol)
try:
values["hc_ratio"] = float(values["num_hetero_atoms"]) / float(values["num_carbons"])
except ZeroDivisionError:
values["hc_ratio"] = 100000000 # if there are zero carbons
values["fc"] = len(list(Brics.FindBRICSBonds(mol))) # how many BRICS bonds, related to complexity
values["is_good"] = True # default to true, but not yet observed
atoms = [atom.GetSymbol() for atom in mol.GetAtoms()] # get all the atoms, and make the list unique (only types)
atoms = set(atoms)
atoms = list(atoms)
values["atoms"] = atoms
values["num_chiral_centers"] = len(Chem.FindMolChiralCenters(mol, includeUnassigned=True))
values["rejections"] = [] # empty list to store the reasons for rejection
return values
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 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 main():
sub_df = pd.read_csv("submissions_final_result.csv")
cmp_ds = []
for _, row in sub_df.iterrows():
cmp_dict = {}
mol = Chem.MolFromSmiles(row['smiles_string'])
cmp_dict['submission_id'] = row['submission_id']
cmp_dict['smiles_string'] = row['smiles_string']
# Lipinski's rule
cmp_dict['h_bond_donor'] = rd.CalcNumLipinskiHBD(
mol) # Lipinski Hbond donor
cmp_dict['h_bond_acceptor'] = rd.CalcNumLipinskiHBA(
mol) # Lipinski Hbond acceptor
cmp_dict['moluclar_mass'] = rd._CalcMolWt(mol) # Molecular Weight
cmp_dict['log_p'] = rd.CalcCrippenDescriptors(mol)[
0] # Partition coefficient
# Topological polar surface area
cmp_dict['topological_polar_surface_area'] = rd.CalcTPSA(mol)
cmp_ds.append(cmp_dict)
result = pd.merge(sub_df,
pd.DataFrame(cmp_ds),
on=['submission_id', 'smiles_string'])
result.to_csv("lipinski_psa_result.csv", index=False, encoding='utf-8')
def get_mol_props(mol: AllChem.Mol):
"""
Get the properties of a molecule.
"""
logP = Descriptors.MolLogP(mol)
tpsa = rdMolDescriptors.CalcTPSA(mol)
alpha = rdMolDescriptors.CalcHallKierAlpha(mol)
MR = Descriptors.MolMR(mol)
asa = rdMolDescriptors.CalcLabuteASA(mol)
return [logP, tpsa, alpha, MR, asa]
def calculate_properties(self, mol):
"""this method calculates basic properties for the smiles
returns : list of int or float (properties)"""
properties = []
properties.append(mol.GetNumAtoms())
properties.append(desc.CalcCrippenDescriptors(mol)[0])
properties.append(desc.CalcTPSA(mol))
properties.append(desc.CalcNumRotatableBonds(mol))
properties.append(desc.CalcFractionCSP3(mol))
return properties
def calculate(self):
tpsa = rdMolDescriptors.CalcTPSA(self.mol)
if self._no_only:
return tpsa
for atom in self.mol.GetAtoms():
atomic_num = atom.GetAtomicNum()
if atomic_num == 15:
tpsa += self._get_phosphorus_contrib(atom)
elif atomic_num == 16:
tpsa += self._get_sulfur_contrib(atom)
return tpsa
def main(in_file, output):
Cmpds = {}
InMols = rdkit_open([in_file])
print('\n # Number of input molecule: {0}'.format(len(InMols)))
for mol in InMols:
m = {}
name = mol.GetProp('_Name').split()[0]
m['Name'] = name
m['Formula'] = rd.CalcMolFormula(mol)
m['SMILES'] = Chem.MolToSmiles(mol)
m['MW'] = rd._CalcMolWt(mol) # Molecular Weight
m['logP'] = rd.CalcCrippenDescriptors(mol)[0] # Partition coefficient
m['HDon'] = rd.CalcNumLipinskiHBD(mol) # Lipinski Hbond donor
m['HAcc'] = rd.CalcNumLipinskiHBA(mol) # Lipinski Hbond acceptor
m['TPSA'] = rd.CalcTPSA(mol) # Topological polar surface area
m['Rotat'] = rd.CalcNumRotatableBonds(mol, strict=True) # Rotatable bond
m['MolRef'] = rd.CalcCrippenDescriptors(mol)[1] # Molar refractivity
m['AliRing'] = rd.CalcNumAliphaticRings(mol) # Aliphatic ring number
m['AroRing'] = rd.CalcNumAromaticRings(mol) # Aromatic ring number
# m['Stereo'] = rd.CalcNumAtomStereoCenters(mol) # Stereo center number
# m['UnspStereo'] = rd.CalcNumUnspecifiedAtomStereoCenters(mol) # unspecified stereo
m['SMILES'] = Chem.MolToSmiles(mol,
isomericSmiles=True, allHsExplicit=False)
Cmpds[name] = m
####################################
df = pd.DataFrame.from_dict(Cmpds, orient='index')
df.index.name = 'Name'
# Columns of data to print out
Columns = [ 'Formula',
'MW', 'logP', 'HDon', 'HAcc', 'TPSA',
'Rotat', 'MolRef', 'AliRing', 'AroRing',
#'Stereo', 'UnspStereo',
'SMILES', ]
reorder = df[Columns]
# Output to CSV
reorder.to_csv( output+'.csv', sep=',', na_rep='NA', encoding='utf-8',
float_format='%.5f', header=True )
# Output to Excel
reorder.to_excel( output+'.xlsx', header=True, na_rep='NA' )
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 calculate_properties(self, smiles=None, mol=None, props=[]):
"""this method calculates basic properties for the mol
returns : error (bool)"""
if len(props) == 0:
return True
if mol is None:
mol = Chem.MolFromSmiles(smiles)
if mol is None:
return True
if 'py_formula' in props:
self.data['py_formula'] = desc.CalcMolFormula(mol)
if 'py_em' in props:
self.data['py_em'] = round(desc.CalcExactMolWt(mol), 5)
if 'py_n_Cl_Br' in props:
all_atoms = []
for atom in mol.GetAtoms():
all_atoms.append(atom.GetSymbol())
n_Cl = all_atoms.count('Cl')
n_Br = all_atoms.count('Br')
self.data['py_n_Cl_Br'] = n_Cl + n_Br
if 'py_na' in props:
self.data['py_na'] = mol.GetNumAtoms()
if 'py_mw' in props:
self.data['py_mw'] = desc._CalcMolWt(mol)
if 'py_fsp3' in props:
self.data['py_fsp3'] = desc.CalcFractionCSP3(mol)
if 'py_rb' in props:
self.data['py_rb'] = desc.CalcNumRotatableBonds(mol)
if 'py_tpsa' in props:
self.data['py_tpsa'] = desc.CalcTPSA(mol)
if 'py_clogp' in props:
self.data['py_clogp'] = desc.CalcCrippenDescriptors(mol)[0]
if 'py_nar' in props:
self.data['py_nar'] = desc.CalcNumAromaticRings(mol)
if 'py_nhba' in props:
self.data['py_nhba'] = desc.CalcNumHBA(mol)
if 'py_nhbd' in props:
self.data['py_nhbd'] = desc.CalcNumHBD(mol)
return False
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 generateCompoundPropertiesTask(structure, debug=False):
if debug:
pydevd.settrace('localhost',
port=6901,
stdoutToServer=True,
stderrToServer=True)
molecule = structure.molecule
if not molecule.compoundProperty:
prop = CompoundProperties(molecule=molecule)
else:
prop = molecule.compoundProperty
saltRemover = SaltRemover()
mol = Chem.MolFromMolBlock(str(structure.molfile))
base = saltRemover.StripMol(mol)
prop.hbd = Descriptors.CalcNumHBD(mol)
prop.hba = Descriptors.CalcNumHBA(mol)
prop.rtb = Descriptors.CalcNumRotatableBonds(mol)
prop.alogp = Crippen.MolLogP(mol)
prop.psa = Descriptors.CalcTPSA(mol)
prop.full_mwt = NewDescriptors.MolWt(mol)
# prop.exact_mass = Descriptors.CalcExactMolWt(mol)
if base.GetNumAtoms():
prop.mw_freebase = NewDescriptors.MolWt(base)
prop.full_molformula = Descriptors.CalcMolFormula(mol)
try:
prop.save()
except IntegrityError as e:
if debug:
print e.message
else:
raise e
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)
tpsa = rdMolDescriptors.CalcTPSA(mol)
dist_hs = recurseMolHCount(mol)
output = [
numRings, nitrogenCount, oxygenCount, carbonCount, boronCount,
phosCount, sulfurCount, fluorCount, iodCount, doubleBonds, surf_area,
mol_weight, tpsa
]
for d in dist_hs:
output.append(dist_hs[d])
return output
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
Algorithm in:
P. Ertl, B. Rohde, P. Selzer
Fast Calculation of Molecular Polar Surface Area as a Sum of Fragment-based
Contributions and Its Application to the Prediction of Drug Transport
Properties, J.Med.Chem. 43, 3714-3717, 2000
Implementation based on the Daylight contrib program tpsa.c
"""
contribs = _pyTPSAContribs(mol,verbose=verbose)
res = 0.0
for contrib in contribs:
res += contrib
return res
_pyTPSA.version="1.0.1"
TPSA=lambda *x,**y:rdMolDescriptors.CalcTPSA(*x,**y)
TPSA.version=rdMolDescriptors._CalcTPSA_version
if __name__ == '__main__':
smis = ['C','CC','CCC','CCCC','CO','CCO','COC']
smis = ['C(=O)O','c1ccccc1']
for smi in smis:
m = Chem.MolFromSmiles(smi)
#print(smi, LabuteASA(m))
print('-----------\n',smi)
#print('M:',['% 4.2f'%x for x in SMR_VSA_(m)])
#print('L:',['% 4.2f'%x for x in SlogP_VSA_(m)])
print('P:',['% 4.2f'%x for x in PEOE_VSA_(m)])
print('P:',['% 4.2f'%x for x in PEOE_VSA_(m)])
print()
# "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,
# custom molecular descriptors
#"ring_size_min": get_min_ring_size,
#"ring_size_max": get_max_ring_size,
"frac_sp3": lambda x: rdMolDesc.CalcFractionCSP3(x),
# HTS filters 1/2 - present in the RDKit Fragments
#"num_aldehyde": Fragments.fr_aldehyde,
'HBD', 'jIndex'
]
for name in prop_names:
d[f'{name}'] = []
for i, s in enumerate(smiles):
if (i % 10000 == 0):
print(i)
m = Chem.MolFromSmiles(s)
if (m == None or 'i' in s or '.' in s):
DUD = DUD.drop(i)
print(s, i)
else:
d['QED'].append(QED.default(m))
d['logP'].append(Crippen.MolLogP(m))
d['molWt'].append(Descriptors.MolWt(m))
d['maxCharge'].append(Descriptors.MaxPartialCharge(m))
d['minCharge'].append(Descriptors.MinPartialCharge(m))
d['valence'].append(Descriptors.NumValenceElectrons(m))
d['TPSA'].append(rdMolDescriptors.CalcTPSA(m))
d['HBA'].append(rdMolDescriptors.CalcNumHBA(m))
d['HBD'].append(rdMolDescriptors.CalcNumHBD(m))
d['jIndex'].append(GraphDescriptors.BalabanJ(m))
df = pd.DataFrame.from_dict(d)
df_merge = pd.merge(df, DUD, on=df.index)
#df_merge.to_csv('/home/mcb/jboitr/data/DUD_full.csv')
df_merge.to_csv('C:/Users/jacqu/Documents/data/DUD_full.csv')
def extract(x, from_smiles):
if from_smiles:
mol = Chem.MolFromSmiles(x)
else:
mol = x
if (mol is None) or (len(mol.GetAtoms()) == 0):
if include_3D:
return [0] * 29
else:
return [0] * 24
else:
logP = Crippen.MolLogP(mol)
refractivity = Crippen.MolMR(mol)
weight = Descriptors.MolWt(mol)
exact_weight = Descriptors.ExactMolWt(mol)
heavy_weight = Descriptors.HeavyAtomMolWt(mol)
heavy_count = Lipinski.HeavyAtomCount(mol)
nhoh_count = Lipinski.NHOHCount(mol)
no_count = Lipinski.NOCount(mol)
hacceptor_count = Lipinski.NumHAcceptors(mol)
hdonor_count = Lipinski.NumHDonors(mol)
hetero_count = Lipinski.NumHeteroatoms(mol)
rotatable_bond_count = Lipinski.NumRotatableBonds(mol)
valance_electron_count = Descriptors.NumValenceElectrons(mol)
amide_bond_count = rdMolDescriptors.CalcNumAmideBonds(mol)
aliphatic_ring_count = Lipinski.NumAliphaticRings(mol)
aromatic_ring_count = Lipinski.NumAromaticRings(mol)
saturated_ring_count = Lipinski.NumSaturatedRings(mol)
aliphatic_cycle_count = Lipinski.NumAliphaticCarbocycles(mol)
aliphaticHetero_cycle_count = Lipinski.NumAliphaticHeterocycles(
mol)
aromatic_cycle_count = Lipinski.NumAromaticCarbocycles(mol)
aromaticHetero_cycle_count = Lipinski.NumAromaticHeterocycles(mol)
saturated_cycle_count = Lipinski.NumSaturatedCarbocycles(mol)
saturatedHetero_cycle_count = Lipinski.NumSaturatedHeterocycles(
mol)
tpsa = rdMolDescriptors.CalcTPSA(mol)
if include_3D:
mol_3D = Chem.AddHs(mol)
AllChem.EmbedMolecule(mol_3D)
AllChem.MMFFOptimizeMolecule(mol_3D)
eccentricity = rdMolDescriptors.CalcEccentricity(mol_3D)
asphericity = rdMolDescriptors.CalcAsphericity(mol_3D)
spherocity = rdMolDescriptors.CalcSpherocityIndex(mol_3D)
inertial = rdMolDescriptors.CalcInertialShapeFactor(mol_3D)
gyration = rdMolDescriptors.CalcRadiusOfGyration(mol_3D)
return [
logP, refractivity, weight, exact_weight, heavy_weight,
heavy_count, nhoh_count, no_count, hacceptor_count,
hdonor_count, hetero_count, rotatable_bond_count,
valance_electron_count, amide_bond_count,
aliphatic_ring_count, aromatic_ring_count,
saturated_ring_count, aliphatic_cycle_count,
aliphaticHetero_cycle_count, aromatic_cycle_count,
aromaticHetero_cycle_count, saturated_cycle_count,
saturatedHetero_cycle_count, tpsa, eccentricity,
asphericity, spherocity, inertial, gyration
]
else:
return [
logP, refractivity, weight, exact_weight, heavy_weight,
heavy_count, nhoh_count, no_count, hacceptor_count,
hdonor_count, hetero_count, rotatable_bond_count,
valance_electron_count, amide_bond_count,
aliphatic_ring_count, aromatic_ring_count,
saturated_ring_count, aliphatic_cycle_count,
aliphaticHetero_cycle_count, aromatic_cycle_count,
aromaticHetero_cycle_count, saturated_cycle_count,
saturatedHetero_cycle_count, tpsa
]
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 loadSDF(sdfPath):
# Create images
#generateImages(sdfPath)
# Create a molecule supplier
suppl = Chem.SDMolSupplier(sdfPath)
# Filter empty entries
sdf = [x for x in suppl if x is not None]
# For each molecule in supplier
for mol in sdf:
data = {}
try:
data['fCharge'] = mol.GetProp('Charge')
except:
data['fCharge'] = Chem.GetFormalCharge(mol)
try:
data['name'] = mol.GetProp('DATABASE_ID')
except:
data['name'] = 'unkown'
try:
data['molMass'] = mol.GetProp('Total Molweight')
except:
data['molMass'] = Descriptors.ExactMolWt(mol)
try:
data['cLogP'] = mol.GetProp('cLogP')
except:
data['cLogP'] = Crippen.MolLogP(mol) # não sei se ta certo
try:
data['cLogS'] = mol.GetProp('cLogS')
except:
data['cLogS'] = 0.0
try:
data['tpsa'] = mol.GetProp('Polar Surface Area')
except:
data['tpsa'] = rdMolDescriptors.CalcTPSA(mol)
try:
data['totalSurfaceArea'] = mol.GetProp('Total Surface Area')
except:
data['totalSurfaceArea'] = rdMolDescriptors.CalcTPSA(mol)
try:
data['hbondAcceptors'] = mol.GetProp('H-Acceptors')
except:
data['hbondAcceptors'] = rdMolDescriptors.CalcNumHBA(mol)
try:
data['hbondDonnors'] = mol.GetProp('H-Donors')
except:
data['hbondDonnors'] = rdMolDescriptors.CalcNumHBD(mol)
try:
data['rotable'] = mol.GetProp('Rotatable Bonds')
except:
data['rotable'] = rdMolDescriptors.CalcNumRotatableBonds(mol)
try:
data['mutagenic'] = mol.GetProp('Mutagenic')
except:
data['mutagenic'] = 'Unknown'
try:
data['tumorigenic'] = mol.GetProp('Tumorigenic')
except:
data['tumorigenic'] = 'Unknown'
try:
data['irritant'] = mol.GetProp('Irritant')
except:
data['irritant'] = 'Unkown'
try:
data['smiles'] = mol.GetProp('SMILES')
except:
data['smiles'] = Chem.MolToSmiles(mol)
try:
data['InChI'] = mol.GetProp('INCHI_IDENTIFIER')
except:
data['InChI'] = inchi.MolToInchi(mol)
try:
data['inchiKey'] = mol.GetProp('INCHI_KEY')
except:
data['inchiKey'] = inchi.MolToInchiKey(mol)
try:
data['nonHAtoms'] = mol.GetProp('Non-H Atoms')
except:
data['nonHAtoms'] = -1 # Não sei calcular
try:
data['numAtoms'] = mol.GetProp('numAtoms')
except:
data['numAtoms'] = mol.GetNumAtoms()
try:
data['stereoCenters'] = mol.GetProp('Stereo Centers')
except:
data['stereoCenters'] = mol.GetNumAtoms()
try:
data['provider'] = mol.GetProp('DATABASE_NAME')
except:
print("Nenhum fornecedor encontrado, o campo é obrigatório!")
continue
tmp = AllChem.Compute2DCoords(mol) # Compute its coordinates
Draw.MolToFile(mol,
os.path.join(settings.FILES_DIR, f'molImages/' + data["inchiKey"] + '.png'),
size=(300,300),
kekulize=True,
wedgeBonds=True,
fitImage=True) # Save it
Draw.MolToFile(mol,
os.path.join(settings.FILES_DIR, f'molThumbs/' + data["inchiKey"] + '.png'),
size=(150,150),
kekulize=True,
wedgeBonds=True,
fitImage=True)
feedDatabase(data)
if Compounds.objects.filter(inChIKey=data['inchiKey']).exists():
if not Compounds.objects.filter(provider=['provider']).exists():
feedDatabase(data)
print("feed1")
# append no sdf da base de dados
a = 1
else:
print("continue123")
continue
else:
a = 1
feedDatabase(data)
print("feed2")
'''except:
def get_tpsa_(mol: Mol) -> float:
return round(rdMolDescriptors.CalcTPSA(mol), round_digs)
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)
