torchdrug

# TorchDrug Use TorchDrug as a modular PyTorch graph-learning stack: 1. load a `datasets.*` dataset, 2. choose a `models.*` representation model, 3. wrap it in a `tasks.*` objective, 4. 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: ```bash 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: ```bash 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](https://torchdrug.ai/docs/installation.html). For a CPU-only PyTorch 2.0 environment, one reproducible wheel combination is: ```bash 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: ```python 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](references/molecular_property_prediction.md). ### 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](references/molecular_property_prediction.md). ### 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](references/molecular_generation.md). ### 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](references/retrosynthesis.md). ### 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](references/knowledge_graphs.md). ### 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](references/protein_modeling.md). ## Rules for reliable TorchDrug code 1. **Follow the 0.2.1 API.** The official docs are not a rolling latest-version site. 2. **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. 3. **Let `Engine` preprocess tasks.** If composing pre-trained tasks without constructing their solvers, call each task's `preprocess()` manually. 4. **Keep paired splits synchronized.** For retrosynthesis, reset the same random seed before splitting reaction and synthon datasets. 5. **Use TorchDrug collation.** Use `data.graph_collate` or `core.Engine`; generic PyTorch collation does not know how to pack TorchDrug graphs. 6. **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. 7. **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](references/core_concepts.md) - [Datasets](references/datasets.md) - [Models and architectures](references/models_architectures.md) - [Molecular property prediction and pretraining](references/molecular_property_prediction.md) - [Protein modeling](references/protein_modeling.md) - [Molecular generation](references/molecular_generation.md) - [Retrosynthesis](references/retrosynthesis.md) - [Knowledge graph reasoning](references/knowledge_graphs.md) ## Upstream sources - [TorchDrug 0.2.1 documentation](https://torchdrug.ai/docs/) - [Tutorial index](https://torchdrug.ai/docs/tutorials/) - [Installation](https://torchdrug.ai/docs/installation.html) - [Package reference](https://torchdrug.ai/docs/api/) - [TorchDrug 0.2.1 release notes](https://github.com/DeepGraphLearning/torchdrug/releases/tag/v0.2.1) ## Citing Scientific Agent Skills This skill is part of Scientific Agent Skills by K-Dense. 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