Is MoleculeNet supported?

Yes, the skill provides quick access patterns for over 30 MoleculeNet benchmark datasets for standardized model evaluation and comparison.

Does it support Graph Neural Networks?

Absolutely, it includes implementation patterns for GCN, GAT, MPNN, and other graph-based architectures optimized for molecular data.

What is the DeepChem Claude Skill?

It is a specialized capability that guides Claude in implementing molecular machine learning workflows, from data loading and featurization to model training and evaluation.

Can I use this for drug discovery tasks?

Yes, it is specifically designed for ADMET prediction, toxicity modeling, and binding affinity prediction using chemical-specific best practices.

How does it handle chemical data splitting?

The skill emphasizes using ScaffoldSplitter, which ensures that training and test sets contain distinct molecular cores to prevent data leakage and provide realistic performance estimates.

DeepChem Molecular ML

Name: DeepChem Molecular ML
Author: Microck

byMicrock

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Ciencia de Datos y ML

Enables advanced molecular machine learning and drug discovery workflows using specialized featurizers and graph neural networks.

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DeepChem is a powerful capability that integrates molecular machine learning into Claude’s development environment, facilitating tasks in chemistry, materials science, and biology. It provides specialized tools for loading chemical data formats like SMILES and SDF, converting molecules into ML-ready representations via fingerprints or graph featurizers, and implementing state-of-the-art models such as Graph Neural Networks (GNNs) and Transformers. This skill is essential for researchers and developers building property prediction models for ADMET or toxicity, performing MoleculeNet benchmarking, or applying transfer learning with pretrained chemical models like ChemBERTa and GROVER.

Características Principales

Molecular featurization including Circular Fingerprints (ECFP) and Graph representations
Implementation of Graph Neural Networks (GNNs) including GCN, GAT, and MPNN
Chemical-specific data splitting methods like ScaffoldSplitter to prevent data leakage
Support for transfer learning with pretrained models such as ChemBERTa and GROVER
Seamless access to MoleculeNet benchmark datasets like Tox21, BBBP, and Delaney
81 GitHub stars

Casos de Uso

Predicting molecular properties such as solubility, toxicity, and binding affinity for drug discovery
Benchmarking new machine learning architectures on standardized chemical datasets
Analyzing protein sequences and crystal structures for materials science applications

Acerca de

Características Principales

Molecular featurization including Circular Fingerprints (ECFP) and Graph representations
Implementation of Graph Neural Networks (GNNs) including GCN, GAT, and MPNN
Chemical-specific data splitting methods like ScaffoldSplitter to prevent data leakage
Support for transfer learning with pretrained models such as ChemBERTa and GROVER
Seamless access to MoleculeNet benchmark datasets like Tox21, BBBP, and Delaney
81 GitHub stars

Casos de Uso

Predicting molecular properties such as solubility, toxicity, and binding affinity for drug discovery
Benchmarking new machine learning architectures on standardized chemical datasets
Analyzing protein sequences and crystal structures for materials science applications