Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.
8.3
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Machine Learning
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Exceptional molecular featurization skill with comprehensive coverage of 100+ featurizers for chemical ML tasks. The description clearly enables CLI invocation with precise use cases (QSAR, virtual screening, similarity search). Task knowledge is outstanding with complete workflows, comparative guidance, and practical examples for traditional ML, deep learning, and cheminformatics. Structure is excellent with clean organization, quick-start guide, and well-referenced supporting documents. Novelty is strong - molecular featurization requires specialized domain knowledge, API familiarity across multiple frameworks (RDKit, scikit-learn, transformers), and understanding of 100+ featurizer options that would consume significant tokens for a CLI agent to navigate independently. The skill meaningfully reduces complexity by providing curated guidance, performance tips, and ready-to-use patterns for a specialized domain.
K-Dense-AI
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