Convert SMILES or RDKit molecules into ML-ready numerical representations.
npx skills add https://github.com/ovachiever/droid-tings --skill molfeat-ovachiever
Please help me install this Agent Skill. Skill: molfeat Source: https://github.com/ovachiever/droid-tings/tree/main/skills/molfeat Command: npx skills add https://github.com/ovachiever/droid-tings --skill molfeat-ovachiever
Molfeat unifies 100+ featurizers and pretrained transformers to convert SMILES or RDKit molecules into machine-learning-ready representations, enabling rapid model development and comparison.
Use cases include building and evaluating ML models on molecular data, docking features, and clustering chemical space.
Featurize a set of SMILES with ECFP fingerprints using a MoleculeTransformer, then downstream ML.
High-intent search queries and answers about installing and using this skill.
Molecular featurization converts SMILES or RDKit molecules into ML-ready numerical representations using 100+ featurizers including fingerprints, descriptors, and graph embeddings. Molfeat unifies these featurizers with scikit-learn compatibility and batch processing to enable rapid model development on molecular data.
Yes. Molfeat provides parallelized batch processing across 100+ featurizers including ECFP fingerprints and chemical descriptors, with scikit-learn compatibility for direct integration into QSAR and QSPR workflows.
Molfeat enables rapid comparison by offering hundreds of featurizers—fingerprints, descriptors, and pretrained transformer embeddings—all accessible through a unified interface with batch processing and caching for efficient similarity searching and chemical space analysis.
Yes. Molfeat includes optional pretrained transformers for generating embeddings, supporting deep learning workflows with state persistence for reproducible pipelines alongside traditional fingerprints and descriptors.
Molfeat provides stateful serialization to save and reload featurization pipelines, ensuring reproducible results and enabling consistent feature generation across multiple model development and evaluation cycles.
ovachieverConvert SMILES or RDKit molecules into ML-ready numerical representations.
jaechang-hitsConvert SMILES strings into numerical representations using over 100 featurizers.
felixboehmConvert chemical structures into machine learning-ready numerical formats.
sanand0Convert chemical structures into machine learning-ready numerical representations.
