Agent skill · research science · davila7
torchdrug
Graph-based drug discovery toolkit. Molecular property prediction (ADMET), protein modeling, knowledge graph reasoning, molecular generation, retrosynthesis, GNNs (GIN, GAT, SchNet), 40+ datasets, for PyTorch-based ML on molecules, proteins, and biomedical graphs.
Why this skill is useful
Provides domain-specific knowledge and integration patterns for drug discovery using graph neural networks that the AI wouldn't reliably generate on its own.
What it needs
Requires torchdrug installed locally. About 6k tokens when loaded. Last updated 2026-08-06. 30,138 stars on the source repository.
What this skill does
TorchDrug Overview TorchDrug is a comprehensive PyTorch-based machine learning toolbox for drug discovery and molecular science. Apply graph neural networks, pre-trained models, and task definitions to molecules, proteins, and biological knowledge graphs, including molecular property prediction, protein modeling, knowledge graph reasoning, molecular generation, retrosynthesis planning, with 40+ curated datasets and 20+ model architectures. When to Use This Skill This skill should be used when working with: Data Types: SMILES strings or molecular structures Protein sequences or 3D structures (PDB files) Chemical reactions and retrosynthesis Biomedical knowledge graphs Drug discovery datasets Tasks: Predicting molecular properties (solubility, toxicity, activity) Protein function or structure prediction Drug-target binding prediction Generating new molecular structures Planning chemical synthesis routes Link prediction in biomedical knowledge bases Training graph neural networks on scientific data Libraries and Integration: TorchDrug is the primary library Often used with RDKit for cheminformatics Compatible with PyTorch and PyTorch Lightning Integrates with AlphaFold and ESM for proteins Getting Started Installation Quick Example Core Capabilities 1. Molecular Property Prediction Predict chemical, physical, and biological properties of molecules from structure. Use Cases: Drug-likeness and ADMET properties Toxicity screening Quantum chemistry properties Binding affinity prediction Key Components: 20+ molecular datasets (BBBP, HIV, Tox21, QM9, etc.) GNN models (GIN, GAT, SchNet) PropertyPrediction and MultipleBinaryClassification tasks Reference: See references/molecularpropertyprediction.md for: Complete dataset catalog Model selection guide Training workflows and best practices Feature engineering details 2. Protein Modeling Work with protein sequences, structures, and properties. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills davila7/torchdrug