/
maplight_gnn.py
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/
maplight_gnn.py
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import numpy as np
from sklearn import preprocessing
from rdkit import Chem
from rdkit import RDLogger
from rdkit.Chem import DataStructs
from rdkit.Chem.rdMolDescriptors import GetHashedMorganFingerprint
from rdkit.Avalon.pyAvalonTools import GetAvalonCountFP
from rdkit.Chem import rdReducedGraphs
from rdkit.ML.Descriptors.MoleculeDescriptors import MolecularDescriptorCalculator
from molfeat.trans.pretrained import PretrainedDGLTransformer
class scaler:
def __init__(self, log=False):
self.log = log
self.offset = None
self.scaler = None
def fit(self, y):
# make the values non-negative
self.offset = np.min([np.min(y), 0.0])
y = y.reshape(-1, 1) - self.offset
# scale the input data
if self.log:
y = np.log10(y + 1.0)
self.scaler = preprocessing.StandardScaler().fit(y)
def transform(self, y):
y = y.reshape(-1, 1) - self.offset
# scale the input data
if self.log:
y = np.log10(y + 1.0)
y_scale = self.scaler.transform(y)
return y_scale
def inverse_transform(self, y_scale):
y = self.scaler.inverse_transform(y_scale.reshape(-1, 1))
if self.log:
y = 10.0**y - 1.0
y = y + self.offset
return y
# from https://github.com/rdkit/rdkit/discussions/3863
def count_to_array(fingerprint):
array = np.zeros((0,), dtype=np.int8)
DataStructs.ConvertToNumpyArray(fingerprint, array)
return array
def get_avalon_fingerprints(molecules, n_bits=1024):
fingerprints = molecules.apply(lambda x: GetAvalonCountFP(x, nBits=n_bits))
fingerprints = fingerprints.apply(count_to_array)
return np.stack(fingerprints.values)
def get_morgan_fingerprints(molecules, n_bits=1024, radius=2):
fingerprints = molecules.apply(lambda x:
GetHashedMorganFingerprint(x, nBits=n_bits, radius=radius))
fingerprints = fingerprints.apply(count_to_array)
return np.stack(fingerprints.values)
def get_erg_fingerprints(molecules):
fingerprints = molecules.apply(rdReducedGraphs.GetErGFingerprint)
return np.stack(fingerprints.values)
# from https://www.blopig.com/blog/2022/06/how-to-turn-a-molecule-into-a-vector-of-physicochemical-descriptors-using-rdkit/
def get_chosen_descriptors():
chosen_descriptors = ['BalabanJ', 'BertzCT', 'Chi0', 'Chi0n', 'Chi0v', 'Chi1',
'Chi1n', 'Chi1v', 'Chi2n', 'Chi2v', 'Chi3n', 'Chi3v', 'Chi4n', 'Chi4v',
'EState_VSA1', 'EState_VSA10', 'EState_VSA11', 'EState_VSA2', 'EState_VSA3',
'EState_VSA4', 'EState_VSA5', 'EState_VSA6', 'EState_VSA7', 'EState_VSA8',
'EState_VSA9', 'ExactMolWt', 'FpDensityMorgan1', 'FpDensityMorgan2',
'FpDensityMorgan3', 'FractionCSP3', 'HallKierAlpha', 'HeavyAtomCount',
'HeavyAtomMolWt', 'Ipc', 'Kappa1', 'Kappa2', 'Kappa3', 'LabuteASA',
'MaxAbsEStateIndex', 'MaxAbsPartialCharge', 'MaxEStateIndex', 'MaxPartialCharge',
'MinAbsEStateIndex', 'MinAbsPartialCharge', 'MinEStateIndex', 'MinPartialCharge',
'MolLogP', 'MolMR', 'MolWt', 'NHOHCount', 'NOCount', 'NumAliphaticCarbocycles',
'NumAliphaticHeterocycles', 'NumAliphaticRings', 'NumAromaticCarbocycles',
'NumAromaticHeterocycles', 'NumAromaticRings', 'NumHAcceptors', 'NumHDonors',
'NumHeteroatoms', 'NumRadicalElectrons', 'NumRotatableBonds',
'NumSaturatedCarbocycles', 'NumSaturatedHeterocycles', 'NumSaturatedRings',
'NumValenceElectrons', 'PEOE_VSA1', 'PEOE_VSA10', 'PEOE_VSA11', 'PEOE_VSA12',
'PEOE_VSA13', 'PEOE_VSA14', 'PEOE_VSA2', 'PEOE_VSA3', 'PEOE_VSA4', 'PEOE_VSA5',
'PEOE_VSA6', 'PEOE_VSA7', 'PEOE_VSA8', 'PEOE_VSA9', 'RingCount', 'SMR_VSA1',
'SMR_VSA10', 'SMR_VSA2', 'SMR_VSA3', 'SMR_VSA4', 'SMR_VSA5', 'SMR_VSA6', 'SMR_VSA7',
'SMR_VSA8', 'SMR_VSA9', 'SlogP_VSA1', 'SlogP_VSA10', 'SlogP_VSA11', 'SlogP_VSA12',
'SlogP_VSA2', 'SlogP_VSA3', 'SlogP_VSA4', 'SlogP_VSA5', 'SlogP_VSA6', 'SlogP_VSA7',
'SlogP_VSA8', 'SlogP_VSA9', 'TPSA', 'VSA_EState1', 'VSA_EState10', 'VSA_EState2',
'VSA_EState3', 'VSA_EState4', 'VSA_EState5', 'VSA_EState6', 'VSA_EState7',
'VSA_EState8', 'VSA_EState9', 'fr_Al_COO', 'fr_Al_OH', 'fr_Al_OH_noTert', 'fr_ArN',
'fr_Ar_COO', 'fr_Ar_N', 'fr_Ar_NH', 'fr_Ar_OH', 'fr_COO', 'fr_COO2', 'fr_C_O',
'fr_C_O_noCOO', 'fr_C_S', 'fr_HOCCN', 'fr_Imine', 'fr_NH0', 'fr_NH1', 'fr_NH2',
'fr_N_O', 'fr_Ndealkylation1', 'fr_Ndealkylation2', 'fr_Nhpyrrole', 'fr_SH',
'fr_aldehyde', 'fr_alkyl_carbamate', 'fr_alkyl_halide', 'fr_allylic_oxid',
'fr_amide', 'fr_amidine', 'fr_aniline', 'fr_aryl_methyl', 'fr_azide', 'fr_azo',
'fr_barbitur', 'fr_benzene', 'fr_benzodiazepine', 'fr_bicyclic', 'fr_diazo',
'fr_dihydropyridine', 'fr_epoxide', 'fr_ester', 'fr_ether', 'fr_furan', 'fr_guanido',
'fr_halogen', 'fr_hdrzine', 'fr_hdrzone', 'fr_imidazole', 'fr_imide', 'fr_isocyan',
'fr_isothiocyan', 'fr_ketone', 'fr_ketone_Topliss', 'fr_lactam', 'fr_lactone',
'fr_methoxy', 'fr_morpholine', 'fr_nitrile', 'fr_nitro', 'fr_nitro_arom',
'fr_nitro_arom_nonortho', 'fr_nitroso', 'fr_oxazole', 'fr_oxime',
'fr_para_hydroxylation', 'fr_phenol', 'fr_phenol_noOrthoHbond', 'fr_phos_acid',
'fr_phos_ester', 'fr_piperdine', 'fr_piperzine', 'fr_priamide', 'fr_prisulfonamd',
'fr_pyridine', 'fr_quatN', 'fr_sulfide', 'fr_sulfonamd', 'fr_sulfone',
'fr_term_acetylene', 'fr_tetrazole', 'fr_thiazole', 'fr_thiocyan', 'fr_thiophene',
'fr_unbrch_alkane', 'fr_urea', 'qed']
return chosen_descriptors
def get_rdkit_features(molecules):
calculator = MolecularDescriptorCalculator(get_chosen_descriptors())
X_rdkit = molecules.apply(
lambda x: np.array(calculator.CalcDescriptors(x)))
X_rdkit = np.vstack(X_rdkit.values)
return X_rdkit
def get_gin_supervised_masking(molecules):
transformer = PretrainedDGLTransformer(kind='gin_supervised_masking', dtype=float)
return transformer(molecules)
def get_fingerprints(smiles):
RDLogger.DisableLog('rdApp.*')
molecules = smiles.apply(Chem.MolFromSmiles)
fingerprints = []
fingerprints.append(get_morgan_fingerprints(molecules))
fingerprints.append(get_avalon_fingerprints(molecules))
fingerprints.append(get_erg_fingerprints(molecules))
fingerprints.append(get_rdkit_features(molecules))
fingerprints.append(get_gin_supervised_masking(molecules))
return np.concatenate(fingerprints, axis=1)