What is TorchDrug used for in Claude Code?

TorchDrug is used to perform specialized machine learning tasks in drug discovery, such as predicting molecular properties, modeling proteins, and generating new chemical structures using graph neural networks.

Does this skill require external libraries?

Yes, it primarily interfaces with the TorchDrug library, which is built on PyTorch, and frequently utilizes RDKit for handling SMILES strings and complex chemical data structures.

Does it support retrosynthesis planning?

Yes, the skill includes specialized models and workflows for retrosynthesis, helping users predict synthetic routes and reaction centers for target molecules.

Can I use this for protein structure prediction?

Yes, TorchDrug supports protein modeling using both primary sequences and 3D structures, and it integrates effectively with structural data from AlphaFold or ESM.

TorchDrug Scientific Computing

Name: TorchDrug Scientific Computing
Author: lifangda

bylifangda

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Data Science & ML

Accelerates drug discovery and molecular research by providing specialized tools for graph neural networks, protein modeling, and chemical property prediction.

About

TorchDrug is a comprehensive machine learning framework built on PyTorch, specifically designed for molecular science and drug discovery. It empowers researchers and developers to apply advanced Graph Neural Networks (GNNs) to tasks such as molecular property prediction (ADMET), protein sequence and structure modeling, biomedical knowledge graph reasoning, and de novo molecular generation. By offering curated datasets, pre-trained models, and standardized task definitions, it streamlines the workflow for complex cheminformatics and bioinformatics projects within the Claude Code environment.

Key Features

Advanced knowledge graph reasoning for drug repurposing and biomedical link prediction.
Specialized modules for de novo molecular generation and retrosynthesis planning.
Comprehensive Graph Neural Network (GNN) library including GIN, GAT, and SchNet architectures.
16 GitHub stars
Protein modeling capabilities for sequence and 3D structure-based tasks.
Automated molecular property prediction for drug-likeness, toxicity, and ADMET screening.

Use Cases

Modeling protein functions and interactions for enzyme engineering or structure prediction.
Predicting chemical properties and biological activity for novel drug candidates.
Mining biomedical knowledge graphs to identify potential drug-disease associations.

About

Key Features

Advanced knowledge graph reasoning for drug repurposing and biomedical link prediction.
Specialized modules for de novo molecular generation and retrosynthesis planning.
Comprehensive Graph Neural Network (GNN) library including GIN, GAT, and SchNet architectures.
16 GitHub stars
Protein modeling capabilities for sequence and 3D structure-based tasks.
Automated molecular property prediction for drug-likeness, toxicity, and ADMET screening.

Use Cases

Modeling protein functions and interactions for enzyme engineering or structure prediction.
Predicting chemical properties and biological activity for novel drug candidates.
Mining biomedical knowledge graphs to identify potential drug-disease associations.