Is parallel processing supported for large datasets?

Absolutely; Molfeat includes built-in support for parallel processing using the n_jobs parameter to handle large molecular libraries efficiently.

Can I use Molfeat with Scikit-learn?

Yes, Molfeat provides scikit-learn compatible transformers, making it easy to integrate featurization directly into machine learning pipelines.

What types of featurizers are available in Molfeat?

It offers a wide range including structural fingerprints (ECFP, MACCS), physical descriptors (RDKit, Mordred), and pretrained deep learning embeddings.

Does this skill support deep learning models?

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

What molecular formats does Molfeat support?

Molfeat primarily works with SMILES strings and RDKit molecule objects to generate numerical representations for machine learning.

Molfeat Molecular Featurization

Name: Molfeat Molecular Featurization
Author: BbgnsurfTech

byBbgnsurfTech

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Data Science & ML

Converts chemical structures into numerical representations for machine learning using over 100 specialized featurizers.

About

Molfeat is a comprehensive Python library integrated as a Claude Code skill to streamline molecular featurization tasks in cheminformatics and drug discovery. It provides a unified interface for over 100 featurizers, including traditional fingerprints like ECFP, interpretable 2D/3D descriptors, and state-of-the-art pretrained embeddings like ChemBERTa and GNNs. By enabling Claude to generate scikit-learn compatible pipelines and parallelized featurization code, this skill empowers researchers and developers to build robust QSAR models, perform virtual screenings, and analyze chemical space with high efficiency and reproducibility.

Key Features

Access to 100+ pre-trained embeddings and hand-crafted featurizers
Scikit-learn compatible transformers for easy ML pipeline integration
Built-in parallel processing and caching for high-performance workflows
Seamless conversion of SMILES strings and RDKit molecules to feature vectors
Support for advanced deep learning models like ChemBERTa and Graphormer
3 GitHub stars

Use Cases

Chemical space analysis including clustering, visualization, and dimensionality reduction
Building QSAR/QSPR models for molecular property prediction
Conducting large-scale virtual screening and similarity searches in compound libraries

About

Key Features

Access to 100+ pre-trained embeddings and hand-crafted featurizers
Scikit-learn compatible transformers for easy ML pipeline integration
Built-in parallel processing and caching for high-performance workflows
Seamless conversion of SMILES strings and RDKit molecules to feature vectors
Support for advanced deep learning models like ChemBERTa and Graphormer
3 GitHub stars

Use Cases

Chemical space analysis including clustering, visualization, and dimensionality reduction
Building QSAR/QSPR models for molecular property prediction
Conducting large-scale virtual screening and similarity searches in compound libraries