---

name: torchdrug description: "Route TorchDrug graph learning, drug discovery, molecular ML, protein, knowledge graph, data, model, and training engine workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

TorchDrug

Use this repo skill when a task involves TorchDrug, TorchProtein, graph neural networks for drug discovery, molecular property prediction, molecular generation, retrosynthesis, protein workflows, knowledge graph completion, or TorchDrug's core.Engine training stack.

Start Here

• Read references/repo-provenance.md before deciding whether this skill is current for a TorchDrug checkout or package version.
• Read references/troubleshooting.md for install/import, PyTorch/PyG compiled packages, RDKit, data downloads, GPU, mps, and package-version pitfalls.
• Run python scripts/check_torchdrug_env.py in the user's TorchDrug environment for a no-download import and capability check.

Install And Verify

TorchDrug 0.2.1 supports Python >=3.7,<3.11 and PyTorch >=1.8.0. Prefer a fresh environment with Python 3.10 or earlier:

python -m pip install torchdrug
python - <<'PY'
import torchdrug
from torchdrug import data
print(torchdrug.__version__)
graph = data.Graph([[0, 1], [1, 0]], num_node=2)
print(graph.num_node, graph.num_edge)
PY

When installing from pip, install PyTorch first, then choose torch-scatter and torch-cluster wheels that match the PyTorch and CUDA/CPU build. Conda users can prefer the documented conda channels because they solve PyTorch, PyG, RDKit, and compiled packages together.

Route By Task

User task	Read
Graph, molecule, protein, dataset, SMILES, sequence, packing, masking, splitting, collator, or data validation tasks	sub-skills/graph-data/SKILL.md
Choosing or customizing graph layers, representation models, readouts, samplers, variadic tensor utilities, or MessagePassingBase subclasses	sub-skills/layers-and-extensions/SKILL.md
Building core.Engine loops, checkpointing, config serialization, logging, CPU/GPU settings, task contracts, or training harnesses	sub-skills/training-engine/SKILL.md
Molecular property prediction, molecular pretraining, generation with GCPN/GraphAF, or USPTO50k retrosynthesis	sub-skills/molecular-workflows/SKILL.md
Knowledge graph triples, FB15k/WN18/YAGO/Hetionet, TransE/RotatE/NeuralLP/KBGAT, negative sampling, or filtered ranking	sub-skills/knowledge-graphs/SKILL.md
Protein sequences/structures, contact prediction, GearNet, ESM, protein property/function, or protein-protein interaction workflows	sub-skills/protein-workflows/SKILL.md

Common Workflows

• For a tiny in-memory smoke test, route to graph-data and run its scripts/smoke_graph_data.py helper.
• For a new molecular prediction project, combine graph-data for the molecule dataset, molecular-workflows for task/model choices, and training-engine for core.Engine save/load.
• For custom graph neural layers, use layers-and-extensions first, then return to training-engine to wire the module into a task.
• For protein GearNet or ESM tasks, use protein-workflows for data/model/task choices and layers-and-extensions only for graph construction internals.
• For knowledge graph reasoning, use knowledge-graphs for model/task/dataset decisions and training-engine for optimizer, evaluation, and checkpoint details.

Safety Boundaries

• Do not run dataset downloads, long training, retrosynthesis generation, benchmark sweeps, or ESM weight downloads unless the user allows network, storage, and compute cost.
• Do not assume gpus=[0] is valid. Use gpus=None for CPU-only work and check torch.cuda.is_available() before GPU examples.
• Do not use Apple mps; TorchDrug documentation says Apple silicon can run on CPU but mps is unsupported.
• Do not tell users to open original TorchDrug docs, tests, or examples as runtime requirements. This skill bundles the needed routes, references, and safe helpers.

Repository Scope

This skill covers public TorchDrug package usage and repo-derived workflows: source APIs under torchdrug/, install metadata, Sphinx docs, tutorials, benchmarks, and representative tests. It intentionally does not cover maintainer release automation, full benchmark reproduction, or external dataset/model-weight acquisition beyond documented prerequisites.