Convert SMILES strings into machine-learning feature vectors using 100+ featurizers.
npx skills add https://github.com/jackspace/ClaudeSkillz --skill molfeat
Please help me install this Agent Skill. Skill: molfeat Source: https://github.com/jackspace/ClaudeSkillz/tree/main/skills/scientific-pkg-molfeat Command: npx skills add https://github.com/jackspace/ClaudeSkillz --skill molfeat
MolFeat unifies 100+ featurizers (ECFP, MACCS, descriptors, pretrained models like ChemBERTa) and provides ready-to-use pipelines to convert SMILES to machine-learning features, speeding up model development and deployment in cheminformatics.
Featurize a small set of SMILES with FPCalculator and MoleculeTransformer, then train a simple model.
High-intent search queries and answers about installing and using this skill.
Featurize SMILES strings into ML-ready vectors using molfeat's 100+ featurizers including ECFP, MACCS, descriptors, and pretrained models like ChemBERTa. Accept both SMILES and RDKit Mol objects, output numerical feature vectors for direct model training.
Yes, molfeat enables unified pipelines that mix fingerprints, descriptors, and embeddings into one representation. Use batch transformers compatible with scikit-learn to combine featurizers and feed the result directly into ML workflows.
Molfeat provides ECFP, MACCS, MAP4, molecular descriptors, and pretrained transformer embeddings—all applicable to QSAR modeling, virtual screening, similarity searching, and chemical-space analysis at scale with parallelization and caching.
Molfeat implements parallel processing, error handling, and caching to featurize large molecular datasets. Batch transformers and state-saving enable reproducible, scaled pipelines for production cheminformatics workflows.
Yes, molfeat exposes pretrained transformers such as ChemBERTa alongside traditional fingerprints and descriptors. Pretrained transformers generate high-quality embeddings for deep-learning pipelines and representation learning.
Molfeat accepts both SMILES strings and RDKit Mol objects as input. The unified API handles molecular structure conversion, so you can work with either format depending on your workflow.
InternScienceConvert IUPAC chemical names to SMILES strings via OPSIN.
adiytharpansaAutomate machine learning model training, tuning, and deployment workflows.
silversteinConvert SMILES or RDKit molecules into numeric feature vectors for machine learning.
pradyumnasagarCalculate molecular fingerprints and physicochemical descriptors from SMILES using RDKit.
