Agent skill · research science · k-dense-ai
molfeat
Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.
Why this skill is useful
Provides domain-specific knowledge for molecular featurization and integration with machine learning workflows that the AI wouldn't reliably generate on its own.
What it needs
Requires datamol, python, pytorch installed locally. About 6k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
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 Installation Use a Python 3.9 or 3.10 environment (molfeat does not install on 3.11+ as of 0.11.0): Optional dependency extras (PyPI): molfeat[dgl] — GNN models (GIN variants); upstream recommends dgl<=2.0 (graphbolt issues in newer DGL) molfeat[graphormer] — Graphormer models molfeat[transformer] — ChemBERTa, ChemGPT, MolT5 molfeat[fcd] — FCD descriptors molfeat[pyg] — PyTorch Geometric featurizers molfeat[viz] — NGLView visualization widgets External featurizers: MAP4 is not bundled in molfeat extras — …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/molfeat