def test_custom_kekulize():
smiles = 'CC=C1c2ccccc2C(=CC)c3ccccc13'
smiles = 'N#CC1=C(SCC(=O)Nc2cccc(Cl)c2)N=C([O-])[C@H](C#N)C12CCCCC2'
mol = MolFromSmiles(smiles)
display(mol)
for atom_idx in range(0,mol.GetNumAtoms()):
bonds = mol.GetAtomWithIdx(atom_idx).GetBonds()
for bond in bonds:
print(bond.GetBondType())
non_aromatic_atoms = find_custom_Kekulize_set(mol, 60, 5)
mol = custom_kekulize(mol,non_aromatic_atoms)
display(mol)
for atom_idx in range(0,mol.GetNumAtoms()):
bonds = mol.GetAtomWithIdx(atom_idx).GetBonds()
for bond in bonds:
print(bond.GetBondType())
def test_force_Kekulize():
df = pd.read_csv('All_Moles_Tested_Data.csv')
i= 0
mol_list = []
for smile in df['smiles']:
mol = MolFromSmiles(smile)
x = find_custom_Kekulize_set(smile, max_atoms= 60, max_degree= 5,printMe = False)
for index in x:
mol.GetAtomWithIdx(index).SetAtomicNum(32)
mol_list.append(mol)
df['mol'] = pd.DataFrame({'mol':mol_list})
unit = 5
for i in range(0,len(df)//unit):
display(PandasTools.FrameToGridImage(df.iloc[i*unit:i*unit+unit],column='mol', legendsCol='',molsPerRow=unit))
if((len(df)%unit>0)*1):
display(PandasTools.FrameToGridImage(df.iloc[len(df)//unit*unit:len(df)],column='mol', legendsCol='',molsPerRow=unit))
def generate_graph(smiles, label=None):
mol = MolFromSmiles(smiles)
if not mol:
raise ValueError("Could not parse SMILES string:", smiles)
SYMBOL = [
'B', 'C', 'N', 'O', 'F', 'Si', 'P', 'S', 'Cl', 'As', 'Se', 'Br', 'Te',
'I', 'At', 'other'
]
HYBRIDIZATION = [
Chem.rdchem.HybridizationType.SP,
Chem.rdchem.HybridizationType.SP2,
Chem.rdchem.HybridizationType.SP3,
Chem.rdchem.HybridizationType.SP3D,
Chem.rdchem.HybridizationType.SP3D2,
'other',
]
num_atom = Chem.RemoveHs(mol).GetNumAtoms()
symbol = np.zeros((num_atom, 16), np.uint8)
hybridization = np.zeros((num_atom, 6), np.uint8)
degree = np.zeros((num_atom, 6), np.uint8)
num_h = np.zeros((num_atom, 5), np.uint8)
chirality = np.zeros((num_atom, 3), np.uint8)
aromatic = np.zeros((num_atom, 1), np.uint8)
formal_charge = np.zeros((num_atom, 1), np.float32)
radical_electrons = np.zeros((num_atom, 1), np.float32)
for i in range(num_atom):
atom = mol.GetAtomWithIdx(i)
symbol[i] = one_of_k_encoding_unk(atom.GetSymbol(), SYMBOL)
hybridization[i] = one_of_k_encoding_unk(atom.GetHybridization(),
HYBRIDIZATION)
degree[i] = one_of_k_encoding_unk(atom.GetDegree(), [0, 1, 2, 3, 4, 5])
num_h[i] = one_of_k_encoding_unk(
atom.GetTotalNumHs(includeNeighbors=True), [0, 1, 2, 3, 4])
try:
chirality[i] = one_of_k_encoding_unk(atom.GetProp('_CIPCode'),
['R', 'S', 'unknown'])
except:
chirality[i] = [0, 0, 0]
aromatic[i] = atom.GetIsAromatic()
formal_charge[i] = atom.GetFormalCharge()
radical_electrons[i] = atom.GetNumRadicalElectrons()
# abundant features
# won't bring substantial change to predictive performance, sometimes even worse
AtomicWeight = np.zeros((num_atom, 1), np.float32)
AtomicNumber = np.zeros((num_atom, 1), np.float32)
Rvdw = np.zeros((num_atom, 1), np.float32)
RCovalent = np.zeros((num_atom, 1), np.float32)
DefaultValence = np.zeros((num_atom, 1), np.float32)
valence = np.zeros((num_atom, 1), np.float32)
NOuterElecs = np.zeros((num_atom, 1), np.float32)
ring = np.zeros((num_atom, 7), np.uint8)
acceptor = np.zeros((num_atom, 1), np.uint8)
donor = np.zeros((num_atom, 1), np.uint8)
for i in range(num_atom):
atom = mol.GetAtomWithIdx(i)
AtomicNum = atom.GetAtomicNum()
AtomicNumber[i] = AtomicNum
AtomicWeight[i] = Chem.GetPeriodicTable().GetAtomicWeight(AtomicNum)
Rvdw[i] = Chem.GetPeriodicTable().GetRvdw(
AtomicNum) # (van der Waals radius)
RCovalent[i] = Chem.GetPeriodicTable().GetRcovalent(
AtomicNum) #(covalent radius)
DefaultValence[i] = Chem.GetPeriodicTable().GetDefaultValence(
AtomicNum)
valence[i] = atom.GetExplicitValence()
NOuterElecs[i] = Chem.GetPeriodicTable().GetNOuterElecs(AtomicNum)
ring[i] = [int(atom.IsInRing()), int(atom.IsInRingSize(3)), \
int(atom.IsInRingSize(4)), int(atom.IsInRingSize(5)), \
int(atom.IsInRingSize(6)), int(atom.IsInRingSize(7)), int(atom.IsInRingSize(8))]
factory = ChemicalFeatures.BuildFeatureFactory(
os.path.join(RDConfig.RDDataDir, 'BaseFeatures.fdef'))
feature = factory.GetFeaturesForMol(mol)
for t in range(0, len(feature)):
if feature[t].GetFamily() == 'Donor':
for i in feature[t].GetAtomIds():
donor[i] = 1
elif feature[t].GetFamily() == 'Acceptor':
for i in feature[t].GetAtomIds():
acceptor[i] = 1
num_bond = mol.GetNumBonds()
if num_bond == 0:
num_bond = 1 # except error caused by CH4, NH3
bond_feat = np.zeros((num_bond * 2, 10), np.int16)
bond_index = np.zeros((num_bond * 2, 2), np.int16)
BOND_TYPE = [
Chem.rdchem.BondType.SINGLE,
Chem.rdchem.BondType.DOUBLE,
Chem.rdchem.BondType.TRIPLE,
Chem.rdchem.BondType.AROMATIC,
]
BOND_STEREO = ["STEREONONE", "STEREOANY", "STEREOZ", "STEREOE"]
ij = 0
for i in range(num_atom):
for j in range(num_atom):
if i == j: continue
bond = mol.GetBondBetweenAtoms(i, j)
if bond is not None:
atom1 = mol.GetAtomWithIdx(i)
atom2 = mol.GetAtomWithIdx(j)
bond_index[ij] = [i, j]
bond_type = one_of_k_encoding(bond.GetBondType(), BOND_TYPE)
bond_ring = [bond.GetIsConjugated(), bond.IsInRing()]
bond_stereo = one_of_k_encoding(str(bond.GetStereo()),
BOND_STEREO)
bond_feat[ij] = bond_type + bond_ring + bond_stereo
ij += 1
graph = Graph(
smiles,
[symbol, hybridization, degree, num_h, chirality, aromatic, formal_charge, radical_electrons, \
AtomicWeight, AtomicNumber, Rvdw, RCovalent, DefaultValence, valence, NOuterElecs, ring, acceptor, donor],
bond_feat,
bond_index,
np.array(label).reshape((1, 1)),
)
return graph