Does it support modern Graph Neural Networks?

Absolutely. It provides specific implementation patterns for GCN, GAT, MPNN, and AttentiveFP models optimized for molecular representations.

How do I use MoleculeNet benchmarks with this skill?

The skill provides standardized workflows to load datasets like Tox21 or Delaney with pre-configured splitters and featurizers for rapid model benchmarking.

Why is ScaffoldSplitter important for molecules?

Unlike random splitting, ScaffoldSplitter ensures that molecules with similar core structures are grouped together, preventing data leakage and ensuring the model generalizes to truly novel structures.

Can this skill help with protein analysis?

Yes, it includes patterns for loading protein sequences via FASTALoader and applying sequence models like ProtBERT for biological property prediction.

DeepChem is an open-source Python library designed to simplify the application of deep learning to chemistry, materials science, and biology.

DeepChem Bioinformatics ML

Name: DeepChem Bioinformatics ML
Author: Microck

byMicrock

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Ciencia de Datos y ML

Empowers drug discovery and molecular analysis by integrating the DeepChem toolkit for property prediction, featurization, and graph neural networks.

Acerca de

This skill transforms Claude into an expert in DeepChem, a premier Python library for applying machine learning to chemistry, materials science, and biology. It provides specialized guidance for loading molecular data formats like SMILES and SDF, selecting optimal featurizers such as Circular Fingerprints or MolGraphConv, and implementing robust model architectures including GNNs and Transformers. By emphasizing scientific best practices like scaffold splitting and MoleculeNet benchmarking, it enables researchers and developers to build accurate models for toxicity, solubility, and ADMET property prediction.

Características Principales

81 GitHub stars
Molecular featurization (Fingerprints, Graph Conv, RDKit descriptors)
Specialized loaders for SMILES, SDF, and protein sequences
Support for Transfer Learning with ChemBERTa and GROVER
Scaffold splitting to prevent data leakage in drug discovery
Access to MoleculeNet benchmark datasets (Tox21, BBBP, HIV)

Casos de Uso

Benchmarking ML models on standardized chemical datasets
Predicting ADMET and toxicity properties for drug candidates
Implementing Graph Neural Networks (GCN, MPNN) for molecular graphs

Acerca de

Características Principales

81 GitHub stars
Molecular featurization (Fingerprints, Graph Conv, RDKit descriptors)
Specialized loaders for SMILES, SDF, and protein sequences
Support for Transfer Learning with ChemBERTa and GROVER
Scaffold splitting to prevent data leakage in drug discovery
Access to MoleculeNet benchmark datasets (Tox21, BBBP, HIV)

Casos de Uso

Benchmarking ML models on standardized chemical datasets
Predicting ADMET and toxicity properties for drug candidates
Implementing Graph Neural Networks (GCN, MPNN) for molecular graphs