Convert molecular structures into numerical features for machine learning.
npx skills add https://github.com/JosephWoodall/noosphere --skill molfeat-josephwoodall
Please help me install this Agent Skill. Skill: molfeat Source: https://github.com/JosephWoodall/noosphere/tree/main/.agent/skills/molfeat Command: npx skills add https://github.com/JosephWoodall/noosphere --skill molfeat-josephwoodall
Molfeat unifies molecular featurization into a single, extensible library, converting chemical structures into machine-learning-ready numerical representations to streamline model development and deployment.
Featurize a list of SMILES with FPCalculator and MoleculeTransformer to obtain a feature matrix.
High-intent search queries and answers about installing and using this skill.
You can convert SMILES strings into numerical features for machine learning by applying molecular featurizers like FPCalculator and MoleculeTransformer to obtain a ready-to-use feature matrix. Molfeat supports batch processing of SMILES or RDKit molecules for QSAR modeling.
Molecular featurization for QSAR and virtual screening includes 100+ featurizers such as molecular fingerprints, physicochemical descriptors, pretrained transformers, and graph-based features. These methods convert chemical structures into scalable numerical representations for model development.
Yes, you can use scikit-learn pipelines for molecular featurization because the library provides scikit-learn compatibility through its MoleculeTransformer API. This allows you to integrate molecular featurization seamlessly into existing machine learning workflows with caching support.
Yes, it supports graph neural networks and pretrained transformer backends through optional dependencies for GNNs and graph-based features. You can apply these advanced featurizers to RDKit molecules for graph-based modeling and virtual screening applications.
The best way to featurize RDKit molecules for similarity searching is using a unified featurization library that applies 100+ calculators and transformers to generate consistent numerical representations. This approach streamlines descriptor-based analyses and similarity comparisons across molecular datasets.
SologaConvert SMILES strings into over 100 molecular fingerprints and descriptors.
galeepConvert SMILES strings and RDKit molecules into numerical features for machine learning.
biomaps-infraConvert SMILES strings and RDKit molecules into numerical representations for machine learning.
dralkhConvert SMILES and RDKit molecules into numerical feature vectors.
