Rdkit Chemistry

Lipinski Rule of Five

def lipinski(smi):
    mol = Chem.MolFromSmiles(smi)
    return {
        'MW <= 500': Descriptors.MolWt(mol) <= 500,
        'LogP <= 5': Descriptors.MolLogP(mol) <= 5,
        'HBD <= 5': rdMolDescriptors.CalcNumHBD(mol) <= 5,
        'HBA <= 10': rdMolDescriptors.CalcNumHBA(mol) <= 10,
    }

Substructure Search

molecules = [Chem.MolFromSmiles(s) for s in ['CCO', 'CC(=O)O', 'c1ccccc1', 'c1ccccc1O']]
pattern = Chem.MolFromSmarts('c1ccccc1')  # Benzene ring
hits = [m for m in molecules if m.HasSubstructMatch(pattern)]

Fingerprints and Similarity

from rdkit.Chem import AllChem, MACCSkeys
from rdkit import DataStructs

mol1 = Chem.MolFromSmiles('CC(=O)Oc1ccccc1C(=O)O')
mol2 = Chem.MolFromSmiles('CC(=O)Nc1ccc(O)cc1')

# Morgan (circular) fingerprints — radius 2 ~ ECFP4
fp1 = AllChem.GetMorganFingerprintAsBitVect(mol1, radius=2, nBits=2048)
fp2 = AllChem.GetMorganFingerprintAsBitVect(mol2, radius=2, nBits=2048)

# MACCS keys
mfp1 = MACCSkeys.GenMACCSKeys(mol1)

# Tanimoto / Dice similarity
tanimoto = DataStructs.TanimotoSimilarity(fp1, fp2)
dice = DataStructs.DiceSimilarity(fp1, fp2)

2D Depiction

from rdkit.Chem import Draw

Draw.MolToFile(mol, 'molecule.png', size=(300, 300))

# Grid of molecules
mols = [Chem.MolFromSmiles(s) for s in ['CCO', 'CC(=O)O', 'c1ccccc1O']]
img = Draw.MolsToGridImage(mols, molsPerRow=3, subImgSize=(300, 300))
img.save('grid.png')

Quick One-liner

python3 -c "
from rdkit import Chem; from rdkit.Chem import Descriptors
mol = Chem.MolFromSmiles('CCO')
print(f'MW: {Descriptors.MolWt(mol):.2f}, LogP: {Descriptors.MolLogP(mol):.2f}')
"

Best Practices

1. Always check MolFromSmiles() return for None (invalid SMILES).
2. Use canonical SMILES for consistent comparisons.
3. Morgan radius=2 (ECFP4) is standard for similarity screening.
4. Sanitize molecules before property calculations.
5. Use Chem.AddHs(mol) before 3D coordinate generation.