molfeat

Convert molecular structures into ML-ready feature vectors across hundreds of featurizers.

22|4|Updated May 25, 2026

Search Tags:#ml #featurization #fingerprints #descriptors #molecules #pretrained-models #chemoinformatics

Ready to run

npx skills add https://github.com/crazymsn/academic-skills --skill molfeat-crazymsn

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: molfeat
Source: https://github.com/crazymsn/academic-skills/tree/main/academic-skills/molfeat
Command: npx skills add https://github.com/crazymsn/academic-skills --skill molfeat-crazymsn

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Molecular featurization for ML across hundreds of featurizers to convert chemical structures into numerical representations for predictive modeling.

Core Features & Use Cases

Extensive suite of featurizers including fingerprints, descriptors, and pretrained transformers for QSAR, virtual screening, and space exploration.
Fast batch processing, caching, and easy integration with scikit-learn and PyTorch for end-to-end ML pipelines.
Use cases span drug discovery, materials science, and cheminformatics research with interpretable descriptors and embeddings.

Quick Start

Install Molfeat, instantiate a featurizer (e.g., FPCalculator) and a MoleculeTransformer, then featurize a SMILES list.

Frequently Asked Questions about molfeat

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

FAQPage Schema

How do I convert molecular structures into ML-ready feature vectors for cheminformatics?▼

Molecular featurization transforms chemical structures into numerical representations. You can apply hundreds of featurizers, including fingerprints, descriptors, and pretrained transformers, to generate ML-ready feature vectors for QSAR and virtual screening workflows.

Does molecular featurization work with scikit-learn and PyTorch pipelines?▼

Yes, molecular featurization supports easy integration with scikit-learn and PyTorch. This allows you to seamlessly incorporate molecular descriptors and embeddings into end-to-end machine learning pipelines for drug discovery and materials science.

What is the best way to batch process SMILES strings for ML model training?▼

Batch processing SMILES strings for ML model training is best handled using fast batched processing and caching. You can instantiate a featurizer and a MoleculeTransformer to convert SMILES lists into numerical feature vectors efficiently.

Can I use pretrained transformers for molecular embeddings in virtual screening?▼

Yes, you can use pretrained transformers for molecular embeddings in virtual screening. The featurization suite supports both traditional interpretable descriptors and pretrained transformer embeddings to explore chemical space and train predictive models.

What types of molecular descriptors are available for clustering and space exploration?▼

An extensive suite of molecular descriptors and fingerprints is available for clustering and space exploration. These featurizers convert molecular structures into interpretable numerical representations suitable for analyzing chemical space across biology and materials science.