lib-molfeat

Name: lib-molfeat
Availability: InStock
Author: biomaps-infra

Convert SMILES strings and RDKit molecules into numerical representations for machine learning.

Updated Mar 13, 2026

Search Tags:#machine learning #deep learning #cheminformatics #fingerprints #descriptors #molecular featurization

Ready to run

npx skills add https://github.com/biomaps-infra/blender-opencode --skill lib-molfeat

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: lib-molfeat
Source: https://github.com/biomaps-infra/blender-opencode/tree/main/.opencode/skills/lib-molfeat
Command: npx skills add https://github.com/biomaps-infra/blender-opencode --skill lib-molfeat

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill simplifies the complex process of converting molecular structures into numerical features, enabling machine learning on chemical data.

Core Features & Use Cases

100+ Featurizers: Access a vast library of fingerprints, descriptors, and deep learning embeddings.
ML-Ready Output: Generate features compatible with scikit-learn, PyTorch, and other ML frameworks.
Use Case: Predict a molecule's properties (e.g., toxicity, solubility) by converting its SMILES string into a numerical vector and feeding it into a machine learning model.

Quick Start

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

Frequently Asked Questions about lib-molfeat

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I convert SMILES strings into numerical vectors for machine learning?▼

To convert SMILES strings into numerical vectors for machine learning, you can use a molecular featurization library that transforms chemical structures into ML-ready features like fingerprints and pre-trained embeddings. This provides scikit-learn compatible output for QSAR modeling.

What is molecular featurization and when do I need it for cheminformatics?▼

Molecular featurization is the process of converting molecular structures into numerical representations. You need it for cheminformatics tasks like deep learning and virtual screening, where machine learning models require numerical input rather than chemical structures.

Can I use RDKit molecules directly with scikit-learn transformers?▼

Yes, you can use RDKit molecules directly with scikit-learn compatible transformers designed for molecular featurization. These transformers convert RDKit molecules into numerical features like descriptors and embeddings for integration into ML pipelines.

Does this library support parallel processing for generating molecular fingerprints?▼

Yes, the library supports parallel processing for generating molecular fingerprints and descriptors. This capability accelerates the featurization of large chemical datasets, ensuring efficient computation of numerical representations for machine learning workflows.

What's the best way to get pre-trained deep learning embeddings for chemical structures?▼

The best way to get pre-trained deep learning embeddings for chemical structures is using a unified featurization library offering over 100 pre-trained embeddings. This allows direct conversion of SMILES strings into numerical vectors for deep learning tasks.