Molfeat

------|------|------------|-------|----------| | ecfp | Fingerprint | 2048 | Fast | General purpose | | maccs | Fingerprint | 167 | Very fast | Scaffold similarity | | desc2D | Descriptors | 200+ | Fast | Interpretable models | | mordred | Descriptors | 1800+ | Medium | Comprehensive features | | map4 | Fingerprint | 1024 | Fast | Large-scale screening | | ChemBERTa-77M-MLM | Deep learning | 768 | Slow* | Transfer learning | | gin-supervised-masking | GNN | Variable | Slow* | Graph-based models |

*First run is slow; subsequent runs benefit from caching

Resources

This skill includes comprehensive reference documentation:

references/api_reference.md

Complete API documentation covering:

• molfeat.calc - All calculator classes and parameters
• molfeat.trans - Transformer classes and methods
• molfeat.store - ModelStore usage
• Common patterns and integration examples
• Performance optimization tips

When to load: Reference when implementing specific calculators, understanding transformer parameters, or integrating with scikit-learn/PyTorch.

references/available_featurizers.md

Comprehensive catalog of all 100+ featurizers organized by category:

• Transformer-based language models (ChemBERTa, ChemGPT)
• Graph neural networks (GIN, Graphormer)
• Molecular descriptors (RDKit, Mordred)
• Fingerprints (ECFP, MACCS, MAP4, and 15+ others)
• Pharmacophore descriptors (CATS, Gobbi)
• Shape descriptors (USR, ElectroShape)
• Scaffold-based descriptors

When to load: Reference when selecting the optimal featurizer for a specific task, exploring available options, or understanding featurizer characteristics.

Search tip: Use grep to find specific featurizer types:

grep -i "chembert" references/available_featurizers.md