What chemical formats does it support?

Molfeat primarily works with SMILES strings and RDKit molecule objects, converting them into various descriptors or fingerprints.

How does Molfeat handle large datasets?

It features a MoleculeTransformer class that supports parallel processing and batch inference to handle large compound libraries efficiently.

Does this skill support deep learning models?

Yes, Molfeat includes pretrained transformers for state-of-the-art models like ChemBERTa, ChemGPT, and various Graph Neural Networks (GNNs).

Can I use Molfeat with scikit-learn?

Absolutely. Molfeat's transformers are designed to be fully compatible with scikit-learn pipelines, making it easy to integrate featurization into standard ML workflows.

Molfeat is a molecular featurization hub that provides a unified interface for converting chemical structures into numerical representations for machine learning.

Molfeat - Molecular Featurization Hub

Name: Molfeat - Molecular Featurization Hub
Author: K-Dense-AI

byK-Dense-AI

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3,718

Data Science & ML

Simplifies molecular featurization for machine learning by providing a unified interface for over 100 descriptors, fingerprints, and pretrained embeddings.

About

Molfeat is a comprehensive Python library designed to bridge the gap between chemical structures and machine learning models. By offering a unified interface for over 100 featurizers—ranging from traditional fingerprints like ECFP to state-of-the-art deep learning embeddings like ChemBERTa—it enables researchers to rapidly convert SMILES strings or RDKit molecules into ML-ready numerical representations. With built-in support for parallel processing, scikit-learn compatibility, and an extensive model store, this skill is essential for bioinformatics and cheminformatics workflows involving QSAR modeling, virtual screening, and chemical space analysis.

Key Features

High-performance batch processing with automatic parallelization
Built-in ModelStore for easy discovery and loading of specialized calculators
Unified access to 100+ molecular featurizers and pretrained embeddings
3,718 GitHub stars
Support for state-of-the-art models including ChemBERTa, ChemGPT, and GNNs
Scikit-learn compatible transformers for seamless ML pipeline integration

Use Cases

Building QSAR/QSPR models for predicting chemical properties and biological activity
Performing molecular similarity searches and chemical space visualization
Conducting large-scale virtual screening to rank compound libraries for drug discovery

About

Key Features

High-performance batch processing with automatic parallelization
Built-in ModelStore for easy discovery and loading of specialized calculators
Unified access to 100+ molecular featurizers and pretrained embeddings
3,718 GitHub stars
Support for state-of-the-art models including ChemBERTa, ChemGPT, and GNNs
Scikit-learn compatible transformers for seamless ML pipeline integration

Use Cases

Building QSAR/QSPR models for predicting chemical properties and biological activity
Performing molecular similarity searches and chemical space visualization
Conducting large-scale virtual screening to rank compound libraries for drug discovery