alterlab-torchdrug

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

# TorchDrug 0.2.1 (last release, Jul 2023) requires Python >=3.7,<3.11 and
# torch >=1.8. It will NOT solve on Python 3.11+ — pin an older interpreter:
uv venv --python 3.10
uv pip install torchdrug==0.2.1 torch

Gotchas:

• Install torch first if the solver struggles; TorchDrug builds graph ops against the installed torch.
• TorchDrug vendors its own data.DataLoader, data.Graph, and core.Engine — reach for those, not the bare PyTorch equivalents (see the loop below).
• It is unmaintained as of 2025; for new Python/torch stacks consider alterlab-torch-geometric or alterlab-deepchem. Use this skill when you specifically need TorchDrug's task/dataset abstractions.