Featurize SMILES molecules into ML-ready vectors for QSAR modeling.
npx skills add https://github.com/SciMate-AI/scicli --skill molfeat-scimate-ai
Please help me install this Agent Skill. Skill: molfeat Source: https://github.com/SciMate-AI/scicli/tree/main/internal/skills/bundled/claude-scientific-skills/skills/molfeat Command: npx skills add https://github.com/SciMate-AI/scicli --skill molfeat-scimate-ai
MolFeat provides a unified platform to convert diverse molecular representations into machine-learning-ready feature vectors, accelerating cheminformatics modeling and data-driven discovery.
Create a MoleculeTransformer with a chosen featurizer and apply it to your SMILES list to obtain feature matrices.
High-intent search queries and answers about installing and using this skill.
To featurize SMILES molecules for QSAR modeling, create a MoleculeTransformer with a chosen featurizer and apply it to your SMILES list to obtain ML-ready feature matrices.
Available molecular featurizers include ECFP, MACCS, MAP4, and Mordred calculators, alongside ChemBERTa, ChemGPT transformers, and GNN-based embeddings for cheminformatics pipelines.
Yes, you can use pretrained molecular transformers like ChemBERTa for virtual screening by applying the MoleculeTransformer to generate embeddings from your SMILES inputs.
Molecular featurization supports integration with standard ML pipelines through flexible workflows using calculators, transformers, and model stores to produce feature vectors for machine learning.
Optional dependencies are not strictly required for molecular featurization, as they are handled via extras, allowing you to install only the specific calculators or transformers you need.
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