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

Getting Started

Installation

TorchDrug 0.2.1 (latest on PyPI, July 2023) requires Python 3.7–3.10 and PyTorch 1.8–2.0. Install PyTorch and torch-scatter / torch-cluster first (wheel URL depends on your PyTorch and CUDA versions — see installation docs).

uv pip install torch
# Match torch/CUDA in the URL, e.g. torch-2.0.0+cu118 or cpu
uv pip install torch-scatter torch-cluster -f https://pytorch-geometric.com/whl/torch-2.0.0+cu118.html
uv pip install torchdrug==0.2.1

On Apple Silicon, compile scatter/cluster from source; TorchDrug runs on CPU only (no MPS). Conda: conda install torchdrug -c milagraph -c conda-forge -c pytorch -c pyg.

Quick Example

import torch
from torchdrug import datasets, models, tasks
from torch.utils.data import DataLoader

# Load molecular dataset
dataset = datasets.BBBP("~/molecule-datasets/")
train_set, valid_set, test_set = dataset.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 PyTorch
optimizer = torch.optim.Adam(task.parameters(), lr=1e-3)
train_loader = DataLoader(train_set, batch_size=32, shuffle=True)

for epoch in range(100):
    for batch in train_loader:
        loss = task(batch)
        optimizer.zero_grad()
        loss.backward()
        optimizer.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

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

# Build graph with spatial edges
graph = protein.residue_graph(
    node_position="ca",
    edge_types=["sequential", "radius"],
    radius_cutoff=10.0
)

With PyTorch Lightning

Wrap tasks for Lightning training:

import pytorch_lightning as pl

class LightningTask(pl.LightningModule):
    def __init__(self, torchdrug_task):
        super().__init__()
        self.task = torchdrug_task

    def training_step(self, batch, batch_idx):
        return self.task(batch)

    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

Version Notes (0.2.1)

• PropertyPrediction.predict() returns original-scale values (not standardized); code written for older TorchDrug may need metric/threshold updates (release notes).
• Dataset constructors prefer atom_feature / bond_feature / mol_feature; node_feature / edge_feature / graph_feature are deprecated aliases.
• EvolutionaryScaleModeling supports ESM-2 checkpoints in addition to ESM-1b.

Resources

Official Documentation: https://torchdrug.ai/docs/ (0.2.1) 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.