Does this skill support Graph Neural Networks?

Absolutely, it includes implementation patterns for GCN, GAT, MPNN, and AttentiveFP models specifically designed for molecular graph data.

Why should I use ScaffoldSplitter instead of RandomSplit?

In drug discovery, ScaffoldSplitter ensures that molecules with similar core structures are not split between training and test sets, preventing data leakage and ensuring realistic performance metrics.

Is transfer learning supported for small datasets?

Yes, it provides guidance for using large-scale pretrained models such as ChemBERTa and GROVER to improve performance on small or novel molecular datasets.

Can I predict molecular toxicity with this skill?

Yes, the skill provides specific patterns for loading datasets like Tox21 and training classification models to predict various toxicological properties.

What is DeepChem in the context of Claude Code?

It is a specialized skill that enables Claude to write, debug, and optimize machine learning code for chemistry and biology using the DeepChem Python library.

DeepChem ML for Chemistry

Name: DeepChem ML for Chemistry
Author: BbgnsurfTech

byBbgnsurfTech

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Ciencia de Datos y ML

Provides a specialized toolkit for applying machine learning to molecular property prediction, drug discovery, and materials science.

Acerca de

DeepChem is a powerful integration for Claude that enables advanced molecular modeling and chemical data analysis within a coding workflow. It streamlines complex scientific tasks including molecular featurization (fingerprints and graphs), dataset splitting with ScaffoldSplitter to ensure scientific validity, and instant access to MoleculeNet benchmarks. Whether predicting ADMET properties, toxicity, or solubility, this skill allows Claude to implement sophisticated Graph Neural Networks (GNNs) and leverage pretrained models like ChemBERTa for high-precision drug discovery and biomolecule analysis.

Características Principales

3 GitHub stars
Automated molecular featurization via fingerprints and graph representations
Integration with MoleculeNet benchmark datasets and standardized splitting
Molecular property prediction for ADMET, toxicity, and solubility
Graph Neural Network (GNN) implementations including GCN, GAT, and MPNN
Support for transfer learning using models like ChemBERTa and GROVER

Casos de Uso

Training Graph Neural Networks on molecular datasets for materials design
Predicting ADMET and toxicity profiles for drug candidate libraries
Benchmarking machine learning models against MoleculeNet standards

Acerca de

Características Principales

3 GitHub stars
Automated molecular featurization via fingerprints and graph representations
Integration with MoleculeNet benchmark datasets and standardized splitting
Molecular property prediction for ADMET, toxicity, and solubility
Graph Neural Network (GNN) implementations including GCN, GAT, and MPNN
Support for transfer learning using models like ChemBERTa and GROVER

Casos de Uso

Training Graph Neural Networks on molecular datasets for materials design
Predicting ADMET and toxicity profiles for drug candidate libraries
Benchmarking machine learning models against MoleculeNet standards