Datamol Cheminformatics Toolkit

Datamol Cheminformatics Toolkit

Overview

Datamol provides a lightweight, Pythonic abstraction layer over RDKit for molecular cheminformatics. It simplifies common drug discovery operations — SMILES parsing, standardization, descriptors, fingerprints, clustering, scaffolds, conformers, and visualization — with sensible defaults, built-in parallelization, and cloud storage support via fsspec. All molecular objects are native rdkit.Chem.Mol instances, ensuring full RDKit compatibility.

When to Use

• Parsing, validating, and standardizing molecular structures from SMILES, SDF, or other formats
• Computing molecular descriptors and fingerprints for ML featurization
• Similarity searching and diversity selection from compound libraries
• Clustering compounds by structural similarity (Butina clustering)
• Scaffold analysis and scaffold-based train/test splitting for ML
• BRICS/RECAP molecular fragmentation for fragment-based design
• 3D conformer generation and analysis
• Visualizing molecules as grids with alignment and highlighting
• Batch processing molecular datasets with parallelization
• For quick gene lookups use gget instead; for advanced substructure queries or custom fingerprints, use RDKit directly

Prerequisites

uv pip install datamol

import datamol as dm
import numpy as np
import pandas as pd

Quick Start

import datamol as dm

# Parse and standardize
mol = dm.to_mol("CC(=O)Oc1ccccc1C(=O)O")  # Aspirin
mol = dm.standardize_mol(mol)
print(dm.to_smiles(mol))  # Canonical SMILES