Agent skill · research science · aiskillstore
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
Adds multiple executable scripts for molecular featurization and integrates with machine learning workflows, which the AI cannot generate on its own.
What it needs
Requires molfeat installed locally. About 6k tokens when loaded. Last updated 2026-08-07. 405 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. 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 Optional dependencies for specific featurizers: molfeat[dgl] - GNN models (GIN variants) molfeat[graphormer] - Graphormer models molfeat[transformer] - ChemBERTa, ChemGPT, MolT5 molfeat[fcd] - FCD descriptors molfeat[map4] - MAP4 fingerprints Core Concepts Molfeat organizes featurization into three hierarchical classes: 1. Calculators (molfeat.calc) Callable objects that convert individual molecules into feature vectors. Accept RDKit Chem.Mol objects or SMILES strings. Use calculators for: Single molecule featurization Custom processing loops Direct feature computation Example: 2. Transformers (molfeat.trans) Scikit-learn compatible transformers that wrap calculators for batch processing with parallelization. Use transformers for: Batch featurization of molecular datasets Integration with scikit-learn pipelines Parallel processing (automatic CPU utilization) Example: 3. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/molfeat