molfeat-molecular-featurization

What problem does it solve?

This Skill addresses the challenge of converting complex molecular structures (SMILES strings) into numerical representations suitable for machine learning models, enabling predictive tasks in drug discovery and materials science.

Core Features & Use Cases

• Diverse Featurization: Generates features using over 100 methods, including fingerprints (ECFP, MACCS), descriptors (RDKit, Mordred), and deep learning embeddings (ChemBERTa, GIN).
• Scikit-learn Compatibility: Integrates seamlessly into existing ML pipelines as scikit-learn compatible transformers.
• Use Case: Predict the binding affinity of a new drug candidate by featurizing its SMILES string using ECFP fingerprints and feeding the resulting vector into a pre-trained regression model.

Quick Start

Use the molfeat-molecular-featurization skill to convert the SMILES string 'CCO' into ECFP features.