Molfeat - Molecular Featurization Hub

Overview

Molfeat is a comprehensive Python library for molecular featurization that unifies 100+ pre-trained embeddings and hand-crafted featurizers. Convert chemical structures (SMILES strings or RDKit molecules) into numerical representations for machine learning tasks including QSAR modeling, virtual screening, similarity searching, and deep learning applications. Features fast parallel processing, scikit-learn compatible transformers, and built-in caching.

Version note: Examples target molfeat 0.11.0 (PyPI stable, May 2025). Requires Python 3.9–3.10 (requires-python caps below 3.11). Depends on datamol ≥0.8.0 and PyTorch ≥1.13. Since 0.8.7, prefer datamol Mol objects over raw rdkit.Chem.Mol. Since 0.10.1, fingerprint calculators use RDKit's rdFingerprintGenerator API internally. Since 0.11.0, pretrained models load in memory and base models are set to PyTorch evaluation mode automatically.

When to Use This Skill

This skill should be used when working with:

• Molecular machine learning: Building QSAR/QSPR models, property prediction
• Virtual screening: Ranking compound libraries for biological activity
• Similarity searching: Finding structurally similar molecules
• Chemical space analysis: Clustering, visualization, dimensionality reduction
• Deep learning: Training neural networks on molecular data
• Featurization pipelines: Converting SMILES to ML-ready representations
• Cheminformatics: Any task requiring molecular feature extraction