mol_property

Prediction of pKa from chemical structure using machine learning approaches.

The repository also include some other functions, such as calculating a variety of molecular properties, and finding a most similar molecule ...

mol_property/
├── pka/    # Prediction of pKa from chemical structure using machine learning approaches.
│
├── similarity/    # molecular similarity: find the most similar molecules
│
├── property_api.py    # calculate some other molecular properties, such as solubility, number of Rotatable Bonds.

Properties

• pKa

ref: Prediction of pKa from chemical structure using free and open-source tools

• logS

ref: github: solubility

install

• python package

git clone https://github.com/TVect/mol_property.git
cd mol_property
python setup.py install

• conda package

reference: https://github.com/fastai/fastai/blob/master/conda/meta.yaml

git clone https://github.com/TVect/mol_property.git
cd mol_property
python setup.py sdist
conda build conda/

Usage

from rdkit import Chem
from mol_property import property_api

smi = "Brc(cc1)cc2c1NCC2"
mol = Chem.MolFromSmiles(smi)

print("logP:", get_logP(mol))
# print("logD:", get_logD(mol))
# print("physioCharge:", get_physioCharge(mol))
print("HBA:", get_numHBA(mol))
print("HBD:", get_numHBD(mol))
print("TPSA:", get_polarSurfaceArea(mol))
print("Rotatable Bond:", get_numRotatableBonds(mol))
print("pKa:", get_pKa(mol))
print("logS:", get_logS(mol))
print("numRings:", get_numRings(mol))
# print("Bioavailability", get_bioavailability(mol))
print("ruleOfFive:", get_ruleOfFive(mol))
print("veberRule:", get_veberRule(mol))
print("ChemicalFormula:", get_chemicalFormula(mol))
print("Mass:", get_molecularMass(mol))
print("smiles:", get_SMILES(mol))