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

•

数据科学与机器学习

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

关于

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.

主要功能

Supports advanced deep learning models including ChemBERTa, ChemGPT, and GNNs
Full integration with scikit-learn pipelines for automated machine learning workflows
1 GitHub stars
Unifies 100+ molecular featurizers and pre-trained embeddings in a single library
Includes built-in parallel processing and caching for high-performance featurization of large datasets
Seamlessly converts SMILES strings and RDKit molecules into ML-ready numerical vectors

使用场景

Performing virtual screening and similarity searches on large chemical libraries
Analyzing chemical space through clustering, visualization, and dimensionality reduction
Building QSAR/QSPR models for biological activity and property prediction

关于

主要功能

Supports advanced deep learning models including ChemBERTa, ChemGPT, and GNNs
Full integration with scikit-learn pipelines for automated machine learning workflows
1 GitHub stars
Unifies 100+ molecular featurizers and pre-trained embeddings in a single library
Includes built-in parallel processing and caching for high-performance featurization of large datasets
Seamlessly converts SMILES strings and RDKit molecules into ML-ready numerical vectors

使用场景

Performing virtual screening and similarity searches on large chemical libraries
Analyzing chemical space through clustering, visualization, and dimensionality reduction
Building QSAR/QSPR models for biological activity and property prediction