Can I use this for protein structure prediction?

Yes, it supports protein structure modeling using architectures like GearNet and integrates with AlphaFold and ESM outputs.

Does this skill support RDKit integration?

Yes, TorchDrug integrates seamlessly with RDKit for cheminformatics tasks and converting between SMILES and molecular structures.

Is TorchDrug compatible with PyTorch Lightning?

Yes, TorchDrug tasks can be wrapped in PyTorch Lightning modules for streamlined training and validation workflows.

What is TorchDrug used for?

TorchDrug is a PyTorch-based toolbox used for drug discovery tasks including molecular property prediction, protein modeling, and knowledge graph reasoning.

Which GNN architectures are included?

It includes a wide variety of models such as GCN, GAT, GIN, RGCN, MPNN, and 3D-aware models like SchNet.

TorchDrug Graph-Based Drug Discovery

Name: TorchDrug Graph-Based Drug Discovery
Author: BbgnsurfTech

byBbgnsurfTech

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데이터 과학 및 ML

Powers AI-driven drug discovery using PyTorch for molecular property prediction, protein modeling, and chemical retrosynthesis.

소개

TorchDrug is a comprehensive machine learning toolbox designed for drug discovery and molecular science, enabling Claude to assist with complex tasks such as predicting ADMET properties, modeling protein structures, and planning chemical synthesis. By leveraging graph neural networks (GNNs) and a collection of over 40 curated datasets, this skill provides a modular framework for researchers and developers to build, train, and evaluate models for molecules, proteins, and biomedical knowledge graphs directly within a PyTorch-based environment.

주요 기능

Protein modeling for function, structure, and interaction analysis
Knowledge graph reasoning for drug repurposing and link prediction
De novo molecular generation using policy-based and autoregressive models
Chemical retrosynthesis planning and synthetic route optimization
3 GitHub stars
Graph Neural Networks (GIN, GAT, SchNet) for molecular property prediction

사용 사례

Conducting de novo drug design and multi-objective lead optimization
Predicting drug-likeness and toxicity (ADMET) for new chemical compounds
Analyzing biomedical knowledge graphs for disease mechanism discovery

소개

주요 기능

Protein modeling for function, structure, and interaction analysis
Knowledge graph reasoning for drug repurposing and link prediction
De novo molecular generation using policy-based and autoregressive models
Chemical retrosynthesis planning and synthetic route optimization
3 GitHub stars
Graph Neural Networks (GIN, GAT, SchNet) for molecular property prediction

사용 사례

Conducting de novo drug design and multi-objective lead optimization
Predicting drug-likeness and toxicity (ADMET) for new chemical compounds
Analyzing biomedical knowledge graphs for disease mechanism discovery