molfeat

Name: molfeat
Availability: InStock
Author: Jack

Convert SMILES strings into machine-learning feature vectors using 100+ featurizers.

16|7|Updated Nov 20, 2025

Search Tags:#SMILES #cheminformatics #transformers #fingerprints #molfeat #molecular-featurization

Ready to run

npx skills add https://github.com/jackspace/ClaudeSkillz --skill molfeat

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: molfeat
Source: https://github.com/jackspace/ClaudeSkillz/tree/main/skills/scientific-pkg-molfeat
Command: npx skills add https://github.com/jackspace/ClaudeSkillz --skill molfeat

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

MolFeat unifies 100+ featurizers (ECFP, MACCS, descriptors, pretrained models like ChemBERTa) and provides ready-to-use pipelines to convert SMILES to machine-learning features, speeding up model development and deployment in cheminformatics.

Core Features & Use Cases

Single API for many featurizers: Mix fingerprints, descriptors, and embeddings.
Batch transformers: Scikit-learn compatible transformers for pipelines.
Performance & reproducibility: Parallel processing, caching, and state saving.
Common workflows: QSAR, virtual screening, similarity search, deep learning embeddings.
Use cases include traditional ML with fingerprints, interpretable descriptors, and transformer-based embeddings.

Quick Start

Featurize a small set of SMILES with FPCalculator and MoleculeTransformer, then train a simple model.

Frequently Asked Questions about molfeat

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

FAQPage Schema

How do I convert SMILES strings into machine learning features?▼

Featurize SMILES strings into ML-ready vectors using molfeat's 100+ featurizers including ECFP, MACCS, descriptors, and pretrained models like ChemBERTa. Accept both SMILES and RDKit Mol objects, output numerical feature vectors for direct model training.

Can I combine multiple fingerprints and descriptors in a single pipeline?▼

Yes, molfeat enables unified pipelines that mix fingerprints, descriptors, and embeddings into one representation. Use batch transformers compatible with scikit-learn to combine featurizers and feed the result directly into ML workflows.

What featurization methods work for QSAR and virtual screening?▼

Molfeat provides ECFP, MACCS, MAP4, molecular descriptors, and pretrained transformer embeddings—all applicable to QSAR modeling, virtual screening, similarity searching, and chemical-space analysis at scale with parallelization and caching.

How do I handle large-scale molecular featurization efficiently?▼

Molfeat implements parallel processing, error handling, and caching to featurize large molecular datasets. Batch transformers and state-saving enable reproducible, scaled pipelines for production cheminformatics workflows.

Can I use pretrained models for molecular embeddings?▼

Yes, molfeat exposes pretrained transformers such as ChemBERTa alongside traditional fingerprints and descriptors. Pretrained transformers generate high-quality embeddings for deep-learning pipelines and representation learning.

Do I need RDKit to use molfeat?▼

Molfeat accepts both SMILES strings and RDKit Mol objects as input. The unified API handles molecular structure conversion, so you can work with either format depending on your workflow.