How to turn a SMILES string into a vector of molecular descriptors using RDKit

Molecular descriptors are quantities associated with small molecules that specify physical or chemical properties of interest. They can be used to numerically describe many different aspects of a molecule such as:

  • molecular graph structure,
  • lipophilicity (logP),
  • molecular refractivity,
  • electrotopological state,
  • druglikeness,
  • fragment profile,
  • molecular charge,
  • molecular surface,
  • …

Vectors whose components are molecular descriptors can be used (amongst other things) as high-level feature representations for molecular machine learning. In my experience, molecular descriptor vectors tend to fall slightly short of more low-level molecular representation methods such as extended-connectivity fingerprints or graph neural networks when it comes to predictive performance on large and medium-sized molecular property prediction data sets. However, one advantage of molecular descriptor vectors is their interpretability; there is a reasonable chance that the meaning of a physicochemical descriptor can be intuitively understood by a chemical expert.

A wide variety of useful molecular descriptors can be automatically and easily computed via RDKit purely on the basis of the SMILES string of a molecule. Here is a code snippet to illustrate how this works:

# import packages
from rdkit import Chem
from rdkit.ML.Descriptors.MoleculeDescriptors import MolecularDescriptorCalculator

# define SMILES string of molecule
smiles_string = "O=C(Cc1cncc2ccccc12)Nc1ccccc1"

# convert SMILES string to RDKit mol object
mol = Chem.MolFromSmiles(smiles_string)

# choose 200 molecular 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']

# create molecular descriptor calculator
mol_descriptor_calculator = MolecularDescriptorCalculator(chosen_descriptors)

# use molecular descriptor calculator on RDKit mol object
list_of_descriptor_vals = list(mol_descriptor_calculator.CalcDescriptors(mol))
print(list_of_descriptor_vals)

The above collection of 200 molecular descriptors was taken from the interesting 2020 paper by Fabian et. al. called Molecular representation learning with language-models and domain-relevant auxiliary tasks which is well worth reading. The given collection encompasses properties from all the physicochemical domains we specified in the bulleted list at the beginning. It represents an excellent starting point to begin playing around with molecular descriptor vectors. Have fun!

Author