Does it support 3D molecular structures?

Yes, DeepChem provides featurizers like the Coulomb Matrix for analyzing 3D structures and crystal properties in materials science.

Which models are best for small chemical datasets?

For datasets with fewer than 1,000 samples, traditional ML models like RandomForest (via SklearnModel) or fine-tuning pre-trained models like ChemBERTa are generally recommended.

What is the DeepChem Claude Code Skill?

It is a specialized capability for Claude that provides domain-specific guidance, best practices, and implementation patterns for using the DeepChem molecular ML toolkit.

Can this skill help with protein or DNA analysis?

Yes, it includes support for loading FASTA files and processing biological sequences using models like ProtBERT for protein property prediction.

Why is ScaffoldSplitter recommended for molecular data?

ScaffoldSplitter ensures that molecules with similar structural frameworks are grouped together, preventing data leakage and providing a more accurate assessment of how a model generalizes to truly new chemicals.

DeepChem Molecular ML

Name: DeepChem Molecular ML
Author: x-cmd

byx-cmd

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データサイエンスとML

Simplifies molecular machine learning and drug discovery tasks using the DeepChem toolkit for property prediction and GNNs.

概要

DeepChem is a comprehensive Python library designed to bridge the gap between machine learning and the life sciences. This skill enables Claude to provide expert guidance on molecular property prediction, featurization, MoleculeNet benchmarks, and the implementation of advanced neural networks. It is ideal for researchers and developers working in drug discovery, materials science, and bioinformatics, offering standardized workflows for processing SMILES data, performing scaffold splitting, and leveraging transfer learning with pre-trained models like ChemBERTa.

主な機能

Molecular data loading and processing for SMILES, SDF, and FASTA formats
Specialized data splitting (ScaffoldSplitter) to prevent molecular data leakage
Advanced featurization including Circular Fingerprints and Graph representations
Standardized access to MoleculeNet benchmarks for ADMET and toxicity prediction
Implementation patterns for GCN, GAT, and MPNN Graph Neural Networks
8 GitHub stars

ユースケース

Fine-tuning pre-trained chemical language models on small molecular datasets
Analyzing materials properties like bandgap and formation energy
Predicting ADMET properties and toxicity for drug discovery pipelines

概要

主な機能

Molecular data loading and processing for SMILES, SDF, and FASTA formats
Specialized data splitting (ScaffoldSplitter) to prevent molecular data leakage
Advanced featurization including Circular Fingerprints and Graph representations
Standardized access to MoleculeNet benchmarks for ADMET and toxicity prediction
Implementation patterns for GCN, GAT, and MPNN Graph Neural Networks
8 GitHub stars

ユースケース

Fine-tuning pre-trained chemical language models on small molecular datasets
Analyzing materials properties like bandgap and formation energy
Predicting ADMET properties and toxicity for drug discovery pipelines