Bio Applied Rdkit Basics

# Example: Install RDKit
# pip install rdkit

from rdkit import Chem
from rdkit.Chem import Draw, Descriptors
from rdkit.Chem import rdMolDescriptors
import pandas as pd
import numpy as np

# Example: Parse SMILES
# smiles_dict
#     'Aspirin':   'CC(O)Oc1ccccc1C(O)O',
#     'Caffeine':  'Cn1cnc2c1c(O)n(C)c(O)n2C',
#     'Ibuprofen': 'CC(C)Cc1ccc(cc1)C(C)C(O)O',
#
# mols  name: Chem.MolFromSmiles(smi) for name, smi in smiles_dict.items()

# Example: Validate (None  invalid SMILES)
# for name, mol in mols.items():
#     print(f'name: valid if mol else INVALID')

Structure Visualization

Draw 2D structures with RDKit Draw. Create a grid image of all three molecules.

from IPython.display import display

# Example: Draw molecule grid
# img  Draw.MolsToGridImage(list(mols.values()), legendslist(mols.keys()),
#                            molsPerRow3, subImgSize(300, 250))
# display(img)

Molecular Descriptors & Lipinski's Rule of Five

Compute MW, LogP, HBD, HBA, TPSA for each molecule. Check Lipinski's Rule of Five for oral bioavailability prediction.

Molecular Fingerprints

Compute Morgan (ECFP4) and MACCS fingerprints. Visualize as bit vectors. Explain radius and nBits parameters.

from rdkit.Chem import AllChem
from rdkit import DataStructs

# Example: Morgan fingerprints (ECFP4: radius2, nBits2048)
# fps  name: AllChem.GetMorganFingerprintAsBitVect(mol, radius2, nBits2048)
#        for name, mol in mols.items()

# Example: MACCS keys
# from rdkit.Chem.MACCSkeys import GenMACCSKeys
# maccs_fps  name: GenMACCSKeys(mol) for name, mol in mols.items()

Tanimoto Similarity Search

Compute pairwise Tanimoto similarity matrix for the three molecules. Query a small library and find the most similar compound.

import seaborn as sns
import matplotlib.pyplot as plt

# Example: Pairwise Tanimoto similarity
# names  list(fps.keys())
# sim_matrix  pd.DataFrame(indexnames, columnsnames, dtypefloat)
# for n1 in names:
#     for n2 in names:
#         sim_matrix.locn1, n2  DataStructs.TanimotoSimilarity(fpsn1, fpsn2)

# sns.heatmap(sim_matrix.astype(float), annotTrue, cmap'Blues', vmin0, vmax1)
# plt.title('Tanimoto Similarity (ECFP4)')
# plt.show()

Summary

Recap molecular representations, descriptors, and fingerprints. Discuss use cases: virtual screening, chemical diversity analysis, lead optimization. Link to QSAR modeling notebook.

Pitfalls

• SMILES canonicalization: Different SMILES can represent the same molecule; always canonicalize before comparison
• Stereochemistry: Ignoring chirality in fingerprints can merge enantiomers with different bioactivity
• Descriptor scaling: Molecular descriptors span different ranges; always standardize before ML