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

Does NOT Trigger

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 torch                        # install torch first (see below)
# torch-scatter / torch-cluster ship only as sdists, so they compile against the
# installed torch — either install from the PyG wheel index or ensure a C++ toolchain:
uv pip install torch-scatter torch-cluster \
  --find-links "https://data.pyg.org/whl/torch-${TORCH_VERSION}+${CUDA}.html"  # e.g. cpu, cu121
uv pip install torchdrug==0.2.1

Gotchas:

• Install torch first; TorchDrug and torch-scatter/torch-cluster build graph ops against the installed torch, and mismatched builds fail at import.
• TorchDrug vendors its own data.DataLoader, data.Graph, and core.Engine — reach for those, not the bare PyTorch equivalents (see the loop below).
• TorchDrug depends on rdkit-pypi, which has no wheels past 2022.09 (Python ≤ 3.11) — another reason the stack is pinned to an older interpreter.
• It is unmaintained (no release since 0.2.1, July 2023); for new Python/torch stacks prefer alterlab-torch-geometric or alterlab-deepchem. Use this skill when you specifically need TorchDrug's task/dataset abstractions (e.g. GearNet, its retrosynthesis pipeline).

Quick Example

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

# Load molecular dataset. MoleculeNet-style datasets such as BBBP have no .split()
# method (only datasets with predefined splits do) — split explicitly:
dataset = datasets.BBBP("~/molecule-datasets/")
lengths = [int(0.8 * len(dataset)), int(0.1 * len(dataset))]
lengths += [len(dataset) - sum(lengths)]
train_set, valid_set, test_set = data.ordered_scaffold_split(dataset, lengths)
# (or torch.utils.data.random_split(dataset, lengths) for a random split)

# Define GNN model
model = models.GIN(
    input_dim=dataset.node_feature_dim,
    hidden_dims=[256, 256, 256],
    edge_input_dim=dataset.edge_feature_dim,
    batch_norm=True,
    readout="mean"
)

# Create property prediction task
task = tasks.PropertyPrediction(
    model,
    task=dataset.tasks,
    criterion="bce",
    metric=["auroc", "auprc"]
)

# Train with a native PyTorch loop.
# preprocess() builds the task's MLP prediction head (and label statistics) — core.Engine
# calls it for you; in a manual loop call it BEFORE creating the optimizer, or the head
# is missing and its parameters are never optimized.
task.preprocess(train_set, valid_set, test_set)
# NOTE: use torchdrug.data.DataLoader (NOT torch.utils.data.DataLoader) — its
# default graph_collate packs data.Graph objects; the stock PyTorch collate cannot.
optimizer = torch.optim.Adam(task.parameters(), lr=1e-3)
train_loader = data.DataLoader(train_set, batch_size=32, shuffle=True)

for epoch in range(100):
    for batch in train_loader:
        # task.forward returns (loss, metric), not a bare tensor.
        loss, metric = task(batch)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

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.

Core Capabilities

1. Molecular Property Prediction

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

Use Cases:

• Drug-likeness and ADMET properties
• Toxicity screening
• Quantum chemistry properties
• Binding affinity prediction

Key Components:

• 20+ molecular datasets (BBBP, HIV, Tox21, QM9, etc.)
• GNN models (GIN, GAT, SchNet)
• PropertyPrediction and MultipleBinaryClassification tasks

Reference: See references/molecular_property_prediction.md for:

• Complete dataset catalog
• Model selection guide
• Training workflows and best practices
• Feature engineering details

2. Protein Modeling

Work with protein sequences, structures, and properties.

Use Cases:

• Enzyme function prediction
• Protein stability and solubility
• Subcellular localization
• Protein-protein interactions
• Structure prediction

Key Components:

• 15+ protein datasets (EnzymeCommission, GeneOntology, PDBBind, etc.)
• Sequence models (ESM, ProteinBERT, ProteinLSTM)
• Structure models (GearNet, SchNet)
• Multiple task types for different prediction levels

Reference: See references/protein_modeling.md for:

• Protein-specific datasets
• Sequence vs structure models
• Pre-training strategies
• Integration with AlphaFold and ESM

3. Knowledge Graph Reasoning

Predict missing links and relationships in biological knowledge graphs.

Use Cases:

• Drug repurposing
• Disease mechanism discovery
• Gene-disease associations
• Multi-hop biomedical reasoning

Key Components:

• General KGs (FB15k, WN18) and biomedical (Hetionet)
• Embedding models (TransE, RotatE, ComplEx)
• KnowledgeGraphCompletion task

Reference: See references/knowledge_graphs.md for:

• Knowledge graph datasets (including Hetionet with 45k biomedical entities)
• Embedding model comparison
• Evaluation metrics and protocols
• Biomedical applications

4. Molecular Generation

Generate novel molecular structures with desired properties.

Use Cases:

• De novo drug design
• Lead optimization
• Chemical space exploration
• Property-guided generation

Key Components:

• Autoregressive generation
• GCPN (policy-based generation)
• GraphAutoregressiveFlow
• Property optimization workflows

Reference: See references/molecular_generation.md for:

• Generation strategies (unconditional, conditional, scaffold-based)
• Multi-objective optimization
• Validation and filtering
• Integration with property prediction

5. Retrosynthesis

Predict synthetic routes from target molecules to starting materials.

Use Cases:

• Synthesis planning
• Route optimization
• Synthetic accessibility assessment
• Multi-step planning

Key Components:

• USPTO-50k reaction dataset
• CenterIdentification (reaction center prediction)
• SynthonCompletion (reactant prediction)
• End-to-end Retrosynthesis pipeline

Reference: See references/retrosynthesis.md for:

• Task decomposition (center ID → synthon completion)
• Multi-step synthesis planning
• Commercial availability checking
• Integration with other retrosynthesis tools

6. Graph Neural Network Models

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

Available Models:

• General GNNs: GCN, GAT, GIN, RGCN, MPNN
• 3D-aware: SchNet, GearNet
• Protein-specific: ESM, ProteinBERT, GearNet
• Knowledge graph: TransE, RotatE, ComplEx, SimplE
• Generative: GraphAutoregressiveFlow

Reference: See references/models_architectures.md for:

• Detailed model descriptions
• Model selection guide by task and dataset
• Architecture comparisons
• Implementation tips

7. Datasets

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

Categories:

• Molecular properties (drug discovery, quantum chemistry)
• Protein properties (function, structure, interactions)
• Knowledge graphs (general and biomedical)
• Retrosynthesis reactions

Reference: See references/datasets.md for:

• Complete dataset catalog with sizes and tasks
• Dataset selection guide
• Loading and preprocessing
• Splitting strategies (random, scaffold)

Common Workflows

Workflow 1: Molecular Property Prediction

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

Steps:

1. Load dataset: datasets.BBBP()
2. Choose model: GIN for molecular graphs
3. Define task: PropertyPrediction with binary classification
4. Train with scaffold split for realistic evaluation
5. Evaluate using AUROC and AUPRC

Navigation: references/molecular_property_prediction.md → Dataset selection → Model selection → Training

Workflow 2: Protein Function Prediction

Scenario: Predict enzyme function from sequence.

Steps:

1. Load dataset: datasets.EnzymeCommission()
2. Choose model: ESM (pre-trained) or GearNet (with structure)
3. Define task: PropertyPrediction with multi-class classification
4. Fine-tune pre-trained model or train from scratch
5. Evaluate using accuracy and per-class metrics

Navigation: references/protein_modeling.md → Model selection (sequence vs structure) → Pre-training strategies

Workflow 3: Drug Repurposing via Knowledge Graphs

Scenario: Find new disease treatments in Hetionet.

Steps:

1. Load dataset: datasets.Hetionet()
2. Choose model: RotatE or ComplEx
3. Define task: KnowledgeGraphCompletion
4. Train with negative sampling
5. Query for "Compound-treats-Disease" predictions
6. Filter by plausibility and mechanism

Navigation: references/knowledge_graphs.md → Hetionet dataset → Model selection → Biomedical applications

Workflow 4: De Novo Molecule Generation

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

Steps:

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

Navigation: references/molecular_generation.md → Conditional generation → Multi-objective optimization

Workflow 5: Retrosynthesis Planning

Scenario: Plan synthesis route for target molecule.

Steps:

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

Navigation: references/retrosynthesis.md → Task types → Multi-step planning

Integration Patterns

With RDKit

Convert between TorchDrug molecules and RDKit:

from torchdrug import data
from rdkit import Chem

# SMILES → TorchDrug molecule
smiles = "CCO"
mol = data.Molecule.from_smiles(smiles)

# TorchDrug → RDKit
rdkit_mol = mol.to_molecule()

# RDKit → TorchDrug
rdkit_mol = Chem.MolFromSmiles(smiles)
mol = data.Molecule.from_molecule(rdkit_mol)

With AlphaFold/ESM

Use predicted structures:

from torchdrug import data, layers
from torchdrug.layers import geometry

# Load AlphaFold predicted structure
protein = data.Protein.from_pdb("AF-P12345-F1-model_v4.pdb")

# Build a residue-level graph with sequential + spatial edges
graph_construction_model = layers.GraphConstruction(
    node_layers=[geometry.AlphaCarbonNode()],
    edge_layers=[
        geometry.SpatialEdge(radius=10.0, min_distance=5),
        geometry.SequentialEdge(max_distance=2),
    ],
    edge_feature="gearnet",
)
# GraphConstruction operates on a packed protein batch, not a bare Protein.
graph = graph_construction_model(data.Protein.pack([protein]))

With PyTorch Lightning

Wrap tasks for Lightning training:

import torch
import pytorch_lightning as pl   # or: import lightning.pytorch as pl

class LightningTask(pl.LightningModule):
    def __init__(self, torchdrug_task):
        super().__init__()
        self.task = torchdrug_task      # call task.preprocess(...) before training

    def training_step(self, batch, batch_idx):
        loss, metric = self.task(batch)  # forward returns (loss, metric)
        return loss

    def validation_step(self, batch, batch_idx):
        pred = self.task.predict(batch)
        target = self.task.target(batch)
        return {"pred": pred, "target": target}

    def configure_optimizers(self):
        return torch.optim.Adam(self.parameters(), lr=1e-3)

Technical Details

For deep dives into TorchDrug's architecture:

Core Concepts: See references/core_concepts.md for:

• Architecture philosophy (modular, configurable)
• Data structures (Graph, Molecule, Protein, PackedGraph)
• Model interface and forward function signature
• Task interface (predict, target, forward, evaluate)
• Training workflows and best practices
• Loss functions and metrics
• Common pitfalls and debugging

Quick Reference Cheat Sheet

Choose Dataset:

• Molecular property → references/datasets.md → Molecular section
• Protein task → references/datasets.md → Protein section
• Knowledge graph → references/datasets.md → Knowledge graph section

Choose Model:

• Molecules → references/models_architectures.md → GNN section → GIN/GAT/SchNet
• Proteins (sequence) → references/models_architectures.md → Protein section → ESM
• Proteins (structure) → references/models_architectures.md → Protein section → GearNet
• Knowledge graph → references/models_architectures.md → KG section → RotatE/ComplEx

Common Tasks:

• Property prediction → references/molecular_property_prediction.md or references/protein_modeling.md
• Generation → references/molecular_generation.md
• Retrosynthesis → references/retrosynthesis.md
• KG reasoning → references/knowledge_graphs.md

Understand Architecture:

• Data structures → references/core_concepts.md → Data Structures
• Model design → references/core_concepts.md → Model Interface
• Task design → references/core_concepts.md → Task Interface

Troubleshooting Common Issues

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

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

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

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

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

Resources

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

Summary

Navigate to the appropriate reference file based on your task:

1. Molecular property prediction → molecular_property_prediction.md
2. Protein modeling → protein_modeling.md
3. Knowledge graphs → knowledge_graphs.md
4. Molecular generation → molecular_generation.md
5. Retrosynthesis → retrosynthesis.md
6. Model selection → models_architectures.md
7. Dataset selection → datasets.md
8. Technical details → core_concepts.md

Each reference provides comprehensive coverage of its domain with examples, best practices, and common use cases.