What is the DeepChem Claude Code skill?

It is a specialized capability that helps Claude guide users through molecular machine learning tasks, data featurization, and model training using the DeepChem library.

Can this skill handle graph neural networks (GNNs)?

Yes, it provides implementation patterns and featurization guidance for GCN, GAT, MPNN, and other graph-based molecular models.

Why should I use ScaffoldSplitter for molecules?

Scaffold splitting ensures that molecules with similar core structures are not split between training and test sets, which prevents data leakage and provides more realistic performance metrics.

Does it support standardized benchmark datasets?

Yes, it includes standardized workflows for loading and benchmarking against common MoleculeNet datasets like Tox21, BBBP, and Delaney.

DeepChem Molecular ML

Name: DeepChem Molecular ML
Author: pur3v4d3r

bypur3v4d3r

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Data Science & ML

Facilitates molecular machine learning and drug discovery workflows using the DeepChem toolkit.

About

The DeepChem skill equips Claude with the expertise to implement advanced chemical informatics and drug discovery workflows. It facilitates molecular property prediction, featurization of SMILES and SDF data into graph representations or fingerprints, and the training of specialized models like Graph Neural Networks (GNNs). This skill is essential for researchers and developers working on ADMET prediction, toxicity modeling, and materials science, offering standardized patterns for MoleculeNet benchmarks and transfer learning with pretrained chemical models.

Key Features

Integration with MoleculeNet benchmark datasets for rapid testing
Scaffold-based data splitting to prevent chemical data leakage
Molecular featurization including Circular Fingerprints and Graph Convolutions
1 GitHub stars
Implementation of Graph Neural Networks like GCN, GAT, and AttentiveFP
Transfer learning workflows using pretrained models like ChemBERTa

Use Cases

Analyzing protein-ligand binding affinities and biological sequences
Predicting drug-likeness and ADMET properties for novel compounds
Designing new materials through crystal structure property prediction

About

Key Features

Integration with MoleculeNet benchmark datasets for rapid testing
Scaffold-based data splitting to prevent chemical data leakage
Molecular featurization including Circular Fingerprints and Graph Convolutions
1 GitHub stars
Implementation of Graph Neural Networks like GCN, GAT, and AttentiveFP
Transfer learning workflows using pretrained models like ChemBERTa

Use Cases

Analyzing protein-ligand binding affinities and biological sequences
Predicting drug-likeness and ADMET properties for novel compounds
Designing new materials through crystal structure property prediction