How does Molfeat handle large datasets?

It includes built-in parallelization (using the n_jobs parameter) and efficient caching mechanisms to process millions of molecules rapidly.

What is the primary use of the Molfeat skill?

Molfeat is used to transform chemical structures (like SMILES) into numerical vectors so they can be used as input for machine learning models in drug discovery and chemistry.

Does Molfeat support modern deep learning models?

Yes, it provides pretrained transformers for state-of-the-art models including ChemBERTa, ChemGPT, MolT5, and various Graph Neural Networks (GNNs).

Is Molfeat compatible with standard data science libraries?

Yes, Molfeat's transformers are designed to be fully compatible with scikit-learn, allowing you to use them directly in pipelines, grid searches, and cross-validation.

Molfeat Molecular Featurization

Name: Molfeat Molecular Featurization
Author: lifangda

bylifangda

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Data Science & ML

Simplifies the conversion of chemical structures into machine learning-ready numerical features using over 100 diverse featurizers.

About

Molfeat is a comprehensive Python library and Claude Code skill designed for cheminformatics and molecular machine learning. It provides a unified interface to over 100 molecular featurizers, ranging from traditional fingerprints like ECFP and MACCS to comprehensive physical descriptors and state-of-the-art pretrained embeddings like ChemBERTa. By integrating parallel processing and scikit-learn compatibility, it streamlines the complex process of preparing chemical data for tasks such as QSAR modeling, virtual screening, and chemical space analysis, making it an essential tool for researchers and developers in drug discovery and molecular property prediction.

Key Features

Support for pretrained deep learning embeddings like ChemBERTa, ChemGPT, and Graphormer
Flexible input handling for SMILES strings and RDKit molecule objects
Scikit-learn compatible transformers for seamless integration into ML pipelines
16 GitHub stars
Access to 100+ molecular featurizers including ECFP, MACCS, and Mordred descriptors
High-performance parallel processing for efficient batch featurization of large datasets

Use Cases

Analyzing chemical space via clustering, visualization, and dimensionality reduction
Building QSAR/QSPR models for molecular property and biological activity prediction
Performing high-throughput virtual screening and chemical similarity searches

About

Key Features

Support for pretrained deep learning embeddings like ChemBERTa, ChemGPT, and Graphormer
Flexible input handling for SMILES strings and RDKit molecule objects
Scikit-learn compatible transformers for seamless integration into ML pipelines
16 GitHub stars
Access to 100+ molecular featurizers including ECFP, MACCS, and Mordred descriptors
High-performance parallel processing for efficient batch featurization of large datasets

Use Cases

Analyzing chemical space via clustering, visualization, and dimensionality reduction
Building QSAR/QSPR models for molecular property and biological activity prediction
Performing high-throughput virtual screening and chemical similarity searches