{"slug":"alterlab-torchdrug","title":"alterlab-torchdrug","summary":"Builds PyTorch-native graph neural networks with TorchDrug for molecules and proteins, exposing custom GNN architectures, task/dataset abstractions, molecular generation, retrosynthesis planning, and knowledge-graph reasoning. Use when a project specifically needs TorchDrug's dat","platform":"Claude","tags":[],"authorName":"LLM Mart","authorSlug":"llm-mart","score":0,"source":"github","price":null,"verified":false,"createdAt":"2026-09-23T18:57:00.147766Z","repo":{"url":"https://github.com/AlterLab-IEU/AlterLab-Academic-Skills","stars":68,"forks":13,"license":"MIT","updatedAt":"2026-09-23T13:42:59Z"},"bodyHtml":"
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name: alterlab-torchdrug\ndescription: Builds PyTorch-native graph neural networks with TorchDrug for molecules and proteins, exposing custom GNN architectures, task/dataset abstractions, molecular generation, retrosynthesis planning, and knowledge-graph reasoning. Use when a project specifically needs TorchDrug's datasets and tasks — GearNet protein-structure models, center-identification/synthon-completion retrosynthesis, or Hetionet knowledge-graph baselines. TorchDrug has had no release since 0.2.1 (July 2023) and needs Python < 3.11, so for new custom GNN work prefer alterlab-torch-geometric, and for ready-made featurizers, MoleculeNet benchmarks, and pre-trained models prefer alterlab-deepchem. Part of the AlterLab Academic Skills suite.\nlicense: Apache-2.0\nallowed-tools: Read Write Edit Bash(python:) Bash(uv:)\ncompatibility: \"Self-contained — runs locally, no API key or account required. TorchDrug 0.2.1 (unmaintained, last release July 2023) requires Python >=3.7,<3.11 (use uv venv --python 3.10) plus torch-scatter/torch-cluster built for the installed torch and a C++ toolchain for its JIT extensions.\"\nmetadata:\nskill-author: AlterLab\nversion: \"1.1.0\"\nlast_updated: \"2026-09-23\"

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TorchDrug

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Overview

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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.

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When to Use This Skill

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This skill should be used when working with:

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Data Types:

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Tasks:

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Libraries and Integration:

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Does NOT Trigger

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ScenarioUse Instead
Building a general custom GNN today (active library, current Python/torch)alterlab-torch-geometric
Ready-made featurizers, MoleculeNet benchmarks, and pretrained models with less codealterlab-deepchem
Only turning molecules into feature matrices for your own modelsalterlab-molfeat
Only sourcing a labeled benchmark dataset (ADMET/DTI) with official splitsalterlab-pytdc
Querying an existing biomedical knowledge graph rather than training a KG modelalterlab-primekg
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Getting Started

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Installation

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# TorchDrug 0.2.1 (last release, Jul 2023) requires Python >=3.7,<3.11 and\n# torch >=1.8. It will NOT solve on Python 3.11+ — pin an older interpreter:\nuv venv --python 3.10\nuv pip install torch                        # install torch first (see below)\n# torch-scatter / torch-cluster ship only as sdists, so they compile against the\n# installed torch — either install from the PyG wheel index or ensure a C++ toolchain:\nuv pip install torch-scatter torch-cluster \\\n  --find-links \"https://data.pyg.org/whl/torch-${TORCH_VERSION}+${CUDA}.html\"  # e.g. cpu, cu121\nuv pip install torchdrug==0.2.1\n
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Gotchas:

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Quick Example

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import torch\nfrom torchdrug import data, datasets, models, tasks\n\n# Load molecular dataset. MoleculeNet-style datasets such as BBBP have no .split()\n# method (only datasets with predefined splits do) — split explicitly:\ndataset = datasets.BBBP(\"~/molecule-datasets/\")\nlengths = [int(0.8 * len(dataset)), int(0.1 * len(dataset))]\nlengths += [len(dataset) - sum(lengths)]\ntrain_set, valid_set, test_set = data.ordered_scaffold_split(dataset, lengths)\n# (or torch.utils.data.random_split(dataset, lengths) for a random split)\n\n# Define GNN model\nmodel = models.GIN(\n    input_dim=dataset.node_feature_dim,\n    hidden_dims=[256, 256, 256],\n    edge_input_dim=dataset.edge_feature_dim,\n    batch_norm=True,\n    readout=\"mean\"\n)\n\n# Create property prediction task\ntask = tasks.PropertyPrediction(\n    model,\n    task=dataset.tasks,\n    criterion=\"bce\",\n    metric=[\"auroc\", \"auprc\"]\n)\n\n# Train with a native PyTorch loop.\n# preprocess() builds the task's MLP prediction head (and label statistics) — core.Engine\n# calls it for you; in a manual loop call it BEFORE creating the optimizer, or the head\n# is missing and its parameters are never optimized.\ntask.preprocess(train_set, valid_set, test_set)\n# NOTE: use torchdrug.data.DataLoader (NOT torch.utils.data.DataLoader) — its\n# default graph_collate packs data.Graph objects; the stock PyTorch collate cannot.\noptimizer = torch.optim.Adam(task.parameters(), lr=1e-3)\ntrain_loader = data.DataLoader(train_set, batch_size=32, shuffle=True)\n\nfor epoch in range(100):\n    for batch in train_loader:\n        # task.forward returns (loss, metric), not a bare tensor.\n        loss, metric = task(batch)\n        optimizer.zero_grad()\n        loss.backward()\n        optimizer.step()\n
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For the standard (non-custom) path, core.Engine(task, train_set, valid_set, test_set, optimizer, batch_size=...) wraps this loop and handles batching/devices. Use the manual loop above when you need full control over the training step.

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Core Capabilities

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1. Molecular Property Prediction

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Predict chemical, physical, and biological properties of molecules from structure.

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Use Cases:

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Key Components:

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Reference: See references/molecular_property_prediction.md for:

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2. Protein Modeling

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Work with protein sequences, structures, and properties.

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Use Cases:

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Key Components:

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Reference: See references/protein_modeling.md for:

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3. Knowledge Graph Reasoning

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Predict missing links and relationships in biological knowledge graphs.

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Use Cases:

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Key Components:

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Reference: See references/knowledge_graphs.md for:

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4. Molecular Generation

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Generate novel molecular structures with desired properties.

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Use Cases:

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Key Components:

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Reference: See references/molecular_generation.md for:

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5. Retrosynthesis

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Predict synthetic routes from target molecules to starting materials.

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Use Cases:

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Key Components:

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Reference: See references/retrosynthesis.md for:

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6. Graph Neural Network Models

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Comprehensive catalog of GNN architectures for different data types and tasks.

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Available Models:

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Reference: See references/models_architectures.md for:

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7. Datasets

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40+ curated datasets spanning chemistry, biology, and knowledge graphs.

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Categories:

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Reference: See references/datasets.md for:

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Common Workflows

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Workflow 1: Molecular Property Prediction

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Scenario: Predict blood-brain barrier penetration for drug candidates.

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Steps:

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  1. Load dataset: datasets.BBBP()
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  2. Choose model: GIN for molecular graphs
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  3. Define task: PropertyPrediction with binary classification
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  4. Train with scaffold split for realistic evaluation
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  5. Evaluate using AUROC and AUPRC
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Navigation: references/molecular_property_prediction.md → Dataset selection → Model selection → Training

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Workflow 2: Protein Function Prediction

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Scenario: Predict enzyme function from sequence.

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Steps:

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  1. Load dataset: datasets.EnzymeCommission()
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  2. Choose model: ESM (pre-trained) or GearNet (with structure)
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  3. Define task: PropertyPrediction with multi-class classification
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  4. Fine-tune pre-trained model or train from scratch
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  5. Evaluate using accuracy and per-class metrics
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Navigation: references/protein_modeling.md → Model selection (sequence vs structure) → Pre-training strategies

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Workflow 3: Drug Repurposing via Knowledge Graphs

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Scenario: Find new disease treatments in Hetionet.

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Steps:

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  1. Load dataset: datasets.Hetionet()
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  2. Choose model: RotatE or ComplEx
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  3. Define task: KnowledgeGraphCompletion
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  4. Train with negative sampling
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  5. Query for \"Compound-treats-Disease\" predictions
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  6. Filter by plausibility and mechanism
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Navigation: references/knowledge_graphs.md → Hetionet dataset → Model selection → Biomedical applications

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Workflow 4: De Novo Molecule Generation

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Scenario: Generate drug-like molecules optimized for target binding.

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Steps:

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  1. Train property predictor on activity data
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  2. Choose generation approach: GCPN for RL-based optimization
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  3. Define reward function combining affinity, drug-likeness, synthesizability
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  4. Generate candidates with property constraints
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  5. Validate chemistry and filter by drug-likeness
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  6. Rank by multi-objective scoring
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Navigation: references/molecular_generation.md → Conditional generation → Multi-objective optimization

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Workflow 5: Retrosynthesis Planning

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Scenario: Plan synthesis route for target molecule.

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Steps:

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  1. Load dataset: datasets.USPTO50k()
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  2. Train center identification model (RGCN)
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  3. Train synthon completion model (GIN)
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  4. Combine into end-to-end retrosynthesis pipeline
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  5. Apply recursively for multi-step planning
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  6. Check commercial availability of building blocks
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Navigation: references/retrosynthesis.md → Task types → Multi-step planning

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Integration Patterns

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With RDKit

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Convert between TorchDrug molecules and RDKit:

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from torchdrug import data\nfrom rdkit import Chem\n\n# SMILES → TorchDrug molecule\nsmiles = \"CCO\"\nmol = data.Molecule.from_smiles(smiles)\n\n# TorchDrug → RDKit\nrdkit_mol = mol.to_molecule()\n\n# RDKit → TorchDrug\nrdkit_mol = Chem.MolFromSmiles(smiles)\nmol = data.Molecule.from_molecule(rdkit_mol)\n
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With AlphaFold/ESM

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Use predicted structures:

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from torchdrug import data, layers\nfrom torchdrug.layers import geometry\n\n# Load AlphaFold predicted structure\nprotein = data.Protein.from_pdb(\"AF-P12345-F1-model_v4.pdb\")\n\n# Build a residue-level graph with sequential + spatial edges\ngraph_construction_model = layers.GraphConstruction(\n    node_layers=[geometry.AlphaCarbonNode()],\n    edge_layers=[\n        geometry.SpatialEdge(radius=10.0, min_distance=5),\n        geometry.SequentialEdge(max_distance=2),\n    ],\n    edge_feature=\"gearnet\",\n)\n# GraphConstruction operates on a packed protein batch, not a bare Protein.\ngraph = graph_construction_model(data.Protein.pack([protein]))\n
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With PyTorch Lightning

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Wrap tasks for Lightning training:

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import torch\nimport pytorch_lightning as pl   # or: import lightning.pytorch as pl\n\nclass LightningTask(pl.LightningModule):\n    def __init__(self, torchdrug_task):\n        super().__init__()\n        self.task = torchdrug_task      # call task.preprocess(...) before training\n\n    def training_step(self, batch, batch_idx):\n        loss, metric = self.task(batch)  # forward returns (loss, metric)\n        return loss\n\n    def validation_step(self, batch, batch_idx):\n        pred = self.task.predict(batch)\n        target = self.task.target(batch)\n        return {\"pred\": pred, \"target\": target}\n\n    def configure_optimizers(self):\n        return torch.optim.Adam(self.parameters(), lr=1e-3)\n
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Technical Details

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For deep dives into TorchDrug's architecture:

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Core Concepts: See references/core_concepts.md for:

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Quick Reference Cheat Sheet

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Choose Dataset:

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Choose Model:

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Common Tasks:

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Understand Architecture:

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Troubleshooting Common Issues

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Issue: Dimension mismatch errors\n→ Check model.input_dim matches dataset.node_feature_dim\n→ See references/core_concepts.md → Essential Attributes

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Issue: Poor performance on molecular tasks\n→ Use scaffold splitting, not random\n→ Try GIN instead of GCN\n→ See references/molecular_property_prediction.md → Best Practices

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Issue: Protein model not learning\n→ Use pre-trained ESM for sequence tasks\n→ Check edge construction for structure models\n→ See references/protein_modeling.md → Training Workflows

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Issue: Memory errors with large graphs\n→ Reduce batch size\n→ Use gradient accumulation\n→ See references/core_concepts.md → Memory Efficiency

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Issue: Generated molecules are invalid\n→ Add validity constraints\n→ Post-process with RDKit validation\n→ See references/molecular_generation.md → Validation and Filtering

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Resources

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Official Documentation: https://torchdrug.ai/docs/\nGitHub: https://github.com/DeepGraphLearning/torchdrug\nPaper: TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery

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Summary

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Navigate to the appropriate reference file based on your task:

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  1. Molecular property prediction → molecular_property_prediction.md
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  2. Protein modeling → protein_modeling.md
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  3. Knowledge graphs → knowledge_graphs.md
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  4. Molecular generation → molecular_generation.md
    5. \n
  5. Retrosynthesis → retrosynthesis.md
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  6. Model selection → models_architectures.md
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  7. Dataset selection → datasets.md
    8. \n
  8. Technical details → core_concepts.md
    9. \n
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Each reference provides comprehensive coverage of its domain with examples, best practices, and common use cases.

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