Is it compatible with scikit-learn?

Absolutely. Molfeat provides scikit-learn compatible transformers that allow you to integrate molecular featurization directly into standard ML pipelines.

What chemical formats does Molfeat support?

Molfeat primarily works with SMILES strings and RDKit molecule objects, converting them into numerical vectors or embeddings for machine learning tasks.

Can I use deep learning models with this skill?

Yes, Molfeat includes specialized transformers for pretrained deep learning models like ChemBERTa, ChemGPT, and various Graph Neural Networks (GNNs).

Does it support parallel processing for large datasets?

Yes, the MoleculeTransformer class supports parallelization via the n_jobs parameter to utilize all available CPU cores for high-speed processing.

Molfeat Molecular Featurization

Name: Molfeat Molecular Featurization
Author: Microck

byMicrock

•

Data Science & ML

Standardizes and automates molecular featurization to convert chemical structures into machine-learning-ready numerical representations.

About

Molfeat is a comprehensive toolkit for molecular machine learning that simplifies the process of featurizing SMILES strings and RDKit molecules. By unifying over 100 diverse featurizers—ranging from traditional fingerprints like ECFP and MACCS to state-of-the-art pretrained transformers like ChemBERTa—it enables researchers to build robust QSAR models, conduct virtual screenings, and analyze chemical space with minimal boilerplate code. It features high-performance parallel processing and seamless integration with the scikit-learn ecosystem, making it ideal for both rapid prototyping and production-scale drug discovery pipelines.

Key Features

Unified access to 100+ hand-crafted and pretrained molecular featurizers
Seamless integration with Scikit-learn pipelines and batch transformers
High-performance parallel processing for large-scale chemical datasets
Support for advanced deep learning embeddings including ChemBERTa and GNNs
Built-in configuration management for reproducible molecular ML experiments
81 GitHub stars

Use Cases

Building QSAR/QSPR models for predicting biological activity and chemical properties
Conducting large-scale virtual screenings to identify potential lead compounds
Visualizing and clustering chemical libraries for diversity and similarity analysis

About

Key Features

Unified access to 100+ hand-crafted and pretrained molecular featurizers
Seamless integration with Scikit-learn pipelines and batch transformers
High-performance parallel processing for large-scale chemical datasets
Support for advanced deep learning embeddings including ChemBERTa and GNNs
Built-in configuration management for reproducible molecular ML experiments
81 GitHub stars

Use Cases

Building QSAR/QSPR models for predicting biological activity and chemical properties
Conducting large-scale virtual screenings to identify potential lead compounds
Visualizing and clustering chemical libraries for diversity and similarity analysis