Convert chemical structures into machine-learning-ready feature vectors for ML workflows.
npx skills add https://github.com/rubensliv/k-dense-ai --skill molfeat-rubensliv
Please help me install this Agent Skill. Skill: molfeat Source: https://github.com/rubensliv/k-dense-ai/tree/main/scientific-skills/molfeat Command: npx skills add https://github.com/rubensliv/k-dense-ai --skill molfeat-rubensliv
Molfeat provides a unified suite of molecular featurization tools to convert chemical structures into machine-learning-ready feature vectors, enabling QSAR modeling, virtual screening, and similarity analysis.
Create a MoleculeTransformer with an FPCalculator (e.g., FPCalculator('ecfp')) and apply it to your SMILES list to obtain feature matrices.
High-intent search queries and answers about installing and using this skill.
Molecular featurization converts SMILES strings into ML-ready feature vectors using FPCalculator and MoleculeTransformer, enabling QSAR modeling. You create a transformer with a specified featurizer and apply it directly to your SMILES list to obtain numpy or pytorch matrices.
Molecular featurizers for virtual screening include traditional fingerprints, descriptor-based features, pharmacophore and shape descriptors, pretrained transformer embeddings, and graph representations. Over 100 featurizers and pretrained embeddings are available to cover diverse chemistry domains.
Yes, molecular featurization provides scikit-learn compatible transformers that support parallel processing and caching. These transformers output numpy or pytorch arrays, allowing seamless integration into end-to-end ML pipelines for clustering, similarity searching, and model deployment.
Yes, pretrained transformer embeddings are supported for molecular similarity search and QSAR modeling. The framework provides calculators and model store components to generate these embeddings, allowing you to compute structural similarities using advanced learned representations.
To scale molecular featurization for large compound libraries, use the built-in parallel processing and caching capabilities of the scikit-learn compatible transformers. This approach accelerates feature vector generation while avoiding redundant computations across your screening workflows.
Yes, molecular featurization supports optional dependencies specifically for graph representations and pretrained transformer embeddings. This modular design ensures you only install the required packages for your specific QSAR or similarity analysis workflow.
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