What is the TorchDrug skill for Claude?

It is a specialized capability that provides Claude with the domain-specific knowledge required to implement drug discovery and molecular modeling workflows using the TorchDrug library.

Does it handle cheminformatics data formats?

Absolutely. The skill includes patterns for handling SMILES strings, PDB files, and converting data between TorchDrug and RDKit objects.

Which scientific models does this skill support?

It supports a wide range of architectures including GIN, GAT, SchNet for molecules, ESM and GearNet for proteins, and RotatE or ComplEx for knowledge graphs.

Is it compatible with PyTorch Lightning?

Yes, the skill provides integration patterns to wrap TorchDrug tasks into PyTorch Lightning modules for scalable training and experimentation.

Can I use this for protein structure prediction?

Yes, it provides workflows for working with protein sequences and 3D structures, including integration with AlphaFold-predicted PDB files and GearNet models.

TorchDrug Scientific Skill

Name: TorchDrug Scientific Skill
Author: K-Dense-AI

byK-Dense-AI

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3,719

데이터 과학 및 ML

Accelerates drug discovery and molecular science workflows using graph neural networks and PyTorch-based modeling.

소개

The TorchDrug skill empowers Claude to act as a specialized assistant for drug discovery and molecular machine learning, providing expert guidance on using the TorchDrug library for complex scientific tasks. It streamlines workflows for molecular property prediction, protein sequence and structure modeling, biomedical knowledge graph reasoning, and de novo molecular generation. By offering implementation patterns for over 20 model architectures and 40 curated datasets, this skill helps researchers and developers build production-grade ML pipelines for ADMET prediction, retrosynthesis planning, and protein-target interaction analysis while maintaining best practices for cheminformatics and bioinformatics.

주요 기능

3,719 GitHub stars
Comprehensive protein modeling using sequence-based (ESM) and 3D structure-based (GearNet) architectures
De novo molecular generation and multi-step retrosynthesis planning
Seamless integration with RDKit, PyTorch Lightning, and AlphaFold structures
Biomedical knowledge graph reasoning for drug repurposing and link prediction
Molecular property prediction for ADMET, toxicity, and quantum chemistry

사용 사례

Discovering novel disease treatments via link prediction in biomedical knowledge bases like Hetionet
Designing synthetic routes for target molecules using automated retrosynthesis planning
Predicting biological properties like blood-brain barrier penetration for drug candidates

소개

주요 기능

3,719 GitHub stars
Comprehensive protein modeling using sequence-based (ESM) and 3D structure-based (GearNet) architectures
De novo molecular generation and multi-step retrosynthesis planning
Seamless integration with RDKit, PyTorch Lightning, and AlphaFold structures
Biomedical knowledge graph reasoning for drug repurposing and link prediction
Molecular property prediction for ADMET, toxicity, and quantum chemistry

사용 사례

Discovering novel disease treatments via link prediction in biomedical knowledge bases like Hetionet
Designing synthetic routes for target molecules using automated retrosynthesis planning
Predicting biological properties like blood-brain barrier penetration for drug candidates