Torchdrug

TorchDrug

Use TorchDrug as a modular PyTorch graph-learning stack:

• load a datasets.* dataset,
• choose a models.* representation model,
• wrap it in a tasks.* objective,
• train and evaluate it with core.Engine.

The current official documentation and latest release are both 0.2.1. Treat

newer Python or PyTorch combinations as unverified rather than silently assuming

compatibility.

Start with the version guard

Before generating or debugging code, inspect the environment:

python --version
python -c "import torch; print(torch.__version__)"
python -c "import torchdrug; print(torchdrug.__version__)"

The supported matrix for TorchDrug 0.2.1 is:

• Python 3.7 through 3.10
• PyTorch 1.8 through 2.0
• Linux, Windows, or macOS
• Apple Silicon: PyTorch 1.13 or later, CPU only; no MPS support

If the project uses Python 3.11+ or PyTorch 2.1+, create a compatible environment

or explicitly test a source build. Do not present such combinations as supported.

Installation

Prefer a dedicated Python 3.10 environment and pin the TorchDrug release:

uv venv --python 3.10
source .venv/bin/activate
uv pip install "torch==2.0.0"

Install torch-scatter and torch-cluster wheels matched to the exact PyTorch

and CUDA pair, following the

official installation page. For a

CPU-only PyTorch 2.0 environment, one reproducible wheel combination is:

uv pip install "torch-scatter==2.1.1" "torch-cluster==1.6.1" \
  --find-links "https://data.pyg.org/whl/torch-2.0.0+cpu.html"
uv pip install "torchdrug==0.2.1"

Do not copy a CUDA wheel URL between environments. Match the PyTorch version,

CUDA build, Python ABI, and platform. On Apple Silicon, the official docs require

building torch-scatter and torch-cluster from source; pin reviewed source

revisions and expect CPU execution.

Canonical property-prediction workflow

Use the documented ClinTox → GIN → PropertyPrediction → Engine pattern:

import torch
from torchdrug import core, datasets, models, tasks

dataset = datasets.ClinTox("~/molecule-datasets/")
lengths = [int(0.8 * len(dataset)), int(0.1 * len(dataset))]
lengths.append(len(dataset) - sum(lengths))
train_set, valid_set, test_set = torch.utils.data.random_split(dataset, lengths)

model = models.GIN(
    input_dim=dataset.node_feature_dim,
    hidden_dims=[256, 256, 256, 256],
    short_cut=True,
    batch_norm=True,
    concat_hidden=True,
)
task = tasks.PropertyPrediction(
    model,
    task=dataset.tasks,
    criterion="bce",
    metric=("auprc", "auroc"),
)

optimizer = torch.optim.Adam(task.parameters(), lr=1e-3)
solver = core.Engine(
    task,
    train_set,
    valid_set,
    test_set,
    optimizer,
    batch_size=1024,
)
solver.train(num_epoch=100)
solver.evaluate("valid")

Add gpus=[0] only when a supported CUDA device is available. Omit gpus for

CPU execution.

For binary classification, task.predict(batch) returns logits; apply

torch.sigmoid when probabilities are needed. In 0.2.1, normalized regression

predictions are returned on the original target scale, which is a breaking change

from older releases.

Choose the official workflow

Molecular property prediction

• Dataset: datasets.ClinTox, BBBP, Tox21, QM9, or another documented

molecule dataset.

• Model: start with models.GIN; use edge_input_dim when the selected feature

configuration supplies edge features.

• Task: tasks.PropertyPrediction.
• Read molecular property prediction.

Self-supervised molecular pretraining

• InfoGraph: models.InfoGraph(gin_model, separate_model=False) wrapped by

tasks.Unsupervised.

• Attribute masking: tasks.AttributeMasking(model, mask_rate=0.15).
• Recreate the same encoder for fine-tuning, then load the checkpoint with

strict=False before training tasks.PropertyPrediction.

• Read molecular property prediction.

Molecule generation

• Dataset: datasets.ZINC250k(..., kekulize=True, atom_feature="symbol").
• GCPN: an models.RGCN encoder wrapped by tasks.GCPNGeneration.
• GraphAF: node and edge models.GraphAF flows wrapped by

tasks.AutoregressiveGeneration.

• Supported optimization tasks in the tutorial are "qed" and "plogp";

criteria are "nll" and/or "ppo".

• Read molecular generation.

Retrosynthesis

• Create two synchronized datasets.USPTO50k views: reaction mode for center

identification and as_synthon=True for synthon completion.

• Train tasks.CenterIdentification and tasks.SynthonCompletion separately.
• Combine the trained tasks with tasks.Retrosynthesis; do not pass raw models

directly to the end-to-end task.

• Read retrosynthesis.

Knowledge graph reasoning

• Embedding workflow: datasets.FB15k237 → models.RotatE →

tasks.KnowledgeGraphCompletion.

• Neural reasoning workflow: models.NeuralLP with fact_ratio=0.75.
• Read knowledge graph reasoning.

Protein modeling

• Build proteins with data.Protein.from_sequence, from_pdb, or

from_molecule.

• Sequence encoders include models.ESM, ProteinCNN, ProteinResNet,

ProteinLSTM, and ProteinBERT; structure encoders include models.GearNet.

• Use documented graph-construction layers rather than a nonexistent

protein.residue_graph() convenience method.

• Read protein modeling.

Rules for reliable TorchDrug code

• Follow the 0.2.1 API. The official docs are not a rolling latest-version

site.

• Prefer documented feature names. Use atom_feature, bond_feature,

residue_feature, and mol_feature; node_feature, edge_feature, and

graph_feature are deprecated aliases in relevant dataset constructors.

• Let Engine preprocess tasks. If composing pre-trained tasks without

constructing their solvers, call each task's preprocess() manually.

• Keep paired splits synchronized. For retrosynthesis, reset the same random

seed before splitting reaction and synthon datasets.

• Use TorchDrug collation. Use data.graph_collate or core.Engine;

generic PyTorch collation does not know how to pack TorchDrug graphs.

• Separate model, task, and engine arguments. A common source of invented

code is passing task options to a model or passing raw models where a composed

task is required.

• Validate generated chemistry. Treat model outputs as candidates, not as

experimentally valid or synthesizable compounds.

Troubleshooting

Installation or import failure

Check Python, PyTorch, torch-scatter, and torch-cluster as one compatibility

set. Most failures are binary-wheel mismatches, unsupported Python versions, or

attempts to use MPS.

Feature dimension mismatch

Build model dimensions from the loaded dataset:

• dataset.node_feature_dim
• dataset.edge_feature_dim
• dataset.num_bond_type
• dataset.num_entity and dataset.num_relation for knowledge graphs

Do not hard-code dimensions copied from a different feature configuration.

Device mismatch

Pass gpus=[0] to core.Engine for supported CUDA execution. For manual

prediction, collate first and move the entire nested batch with utils.cuda.

Checkpoint mismatch

Recreate the same model and feature configuration. For pretraining-to-fine-tuning

transfer, load the checkpoint's "model" state with strict=False; for a complete

solver, use solver.save() and solver.load().

Reference index

• Core concepts and data structures
• Datasets
• Models and architectures
• Molecular property prediction and pretraining
• Protein modeling
• Molecular generation
• Retrosynthesis
• Knowledge graph reasoning

Upstream sources

• TorchDrug 0.2.1 documentation
• Tutorial index
• Installation
• Package reference
• TorchDrug 0.2.1 release notes