How do I handle invalid SMILES strings during featurization?

You can set the ignore_errors=True parameter in the MoleculeTransformer to skip invalid structures while logging errors, ensuring your pipeline doesn't crash on bad data.

Is Molfeat compatible with scikit-learn?

Yes, Molfeat provides transformers that are fully compatible with scikit-learn pipelines, allowing you to include featurization as a standard step in your machine learning models.

What molecular representations does Molfeat support?

Molfeat supports a wide range of representations including ECFP/FCFP fingerprints, MACCS keys, RDKit and Mordred descriptors, and state-of-the-art deep learning embeddings like ChemBERTa and Graphormer.

Can Molfeat handle large datasets efficiently?

Absolutely. It features built-in parallel processing through the n_jobs parameter and includes caching mechanisms to handle high-volume molecular data quickly.

Does this skill support 3D molecular featurization?

Yes, Molfeat includes specialized calculators for 3D features such as USR (Ultrafast Shape Recognition) and USRCAT descriptors.

Molecular Featurization (Molfeat)

Name: Molecular Featurization (Molfeat)
Author: pur3v4d3r

bypur3v4d3r

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Data Science & ML

Provides a comprehensive suite of 100+ molecular featurizers for converting chemical structures into machine learning-ready numerical representations.

Molfeat is a specialized hub for cheminformatics and molecular machine learning, offering a unified interface for over 100 featurizers including ECFP fingerprints, RDKit descriptors, and deep learning embeddings like ChemBERTa. It streamlines the workflow of converting SMILES strings or RDKit molecules into feature vectors, facilitating tasks like QSAR modeling, virtual screening, and chemical space analysis with built-in parallel processing and scikit-learn compatibility. This skill is essential for researchers and developers working on drug discovery, property prediction, and chemical data science.

Key Features

01Supports advanced deep learning models including ChemBERTa, ChemGPT, and GNNs

02Full integration with scikit-learn pipelines for automated machine learning workflows

031 GitHub stars

04Unifies 100+ molecular featurizers and pre-trained embeddings in a single library

05Includes built-in parallel processing and caching for high-performance featurization of large datasets

06Seamlessly converts SMILES strings and RDKit molecules into ML-ready numerical vectors

Use Cases

01Performing virtual screening and similarity searches on large chemical libraries

02Analyzing chemical space through clustering, visualization, and dimensionality reduction

03Building QSAR/QSPR models for biological activity and property prediction

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add pur3v4d3r/pur3-pkb-codebase molfeat

For use in Claude.ai and ChatGPT

Download Skill