torchdrug

Overview

TorchDrug is a comprehensive machine learning framework for drug discovery built on PyTorch. It provides graph-based molecular representations (atoms as nodes, bonds as edges), a library of graph neural network (GNN) architectures, benchmark datasets, and pretrained models for tasks including molecular property prediction, drug-target interaction, retrosynthesis, and generative molecular design. TorchDrug integrates with PyTorch Lightning and standard ML tooling, making it accessible to both computational chemists and ML practitioners.

When to Use

• Molecular property prediction: Training or fine-tuning GNN models to predict ADMET properties (solubility, toxicity, permeability) or bioactivity (IC50, Ki) from molecular graphs.
• Drug-target interaction (DTI) prediction: Building models that predict binding affinity between a compound (SMILES) and a protein (sequence or structure).
• Retrosynthesis prediction: Identifying plausible synthetic routes for a target molecule using template-based or template-free models.
• Pretraining on large molecular datasets: Leveraging pretrained GNN representations on ChEMBL or ZINC for transfer learning to small datasets.
• Molecular generation: Training graph-based generative models (GCPN, GraphAF) to design novel molecules with desired properties.
• Benchmarking GNN architectures: Comparing GraphConv, MPNN, GAT, AttentiveFP on standard MoleculeNet tasks.
• For fast fingerprint-based property prediction without deep learning, use RDKit + scikit-learn instead.
• For protein structure tasks (folding, docking), use ESMFold or DiffDock rather than TorchDrug.

Prerequisites

• Python packages: torchdrug, torch, torch-geometric, rdkit
• Environment: Python 3.8+, CUDA-compatible GPU recommended for training
• Data requirements: SMILES strings or molecular SDF files; protein sequences for DTI tasks

pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu118
pip install torch-geometric
pip install torchdrug
pip install rdkit

Quick Start

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

# Load a benchmark dataset and train a GNN for property prediction
dataset = datasets.BBBP("~/data/bbbp", node_feature="default", edge_feature="default")
print(f"Dataset: {len(dataset)} molecules, task: BBBP (blood-brain barrier penetration)")

# Define model: GIN encoder
model = models.GIN(
    input_dim=dataset.node_feature_dim,
    hidden_dims=[256, 256],
    short_cut=True,
    batch_norm=True,
    concat_hidden=True,
)

# Define training task
task = tasks.PropertyPrediction(
    model, task=dataset.tasks,
    criterion="bce", metric=("auprc", "auroc"),
)

# Train with the Solver
optimizer = torch.optim.Adam(task.parameters(), lr=1e-3)
solver    = core.Engine(task, dataset, None, None, optimizer, gpus=[0])
solver.train(num_epoch=50)
print("Training complete")

Core API

Module 1: Molecular Graph Representation

TorchDrug represents molecules as typed graphs. data.Molecule is the core data structure.

from torchdrug import data
from rdkit import Chem

# Create a molecule from SMILES
smiles = "CC(=O)Oc1ccccc1C(=O)O"   # aspirin
mol    = data.Molecule.from_smiles(smiles, node_feature="default", edge_feature="default")

print(f"Atoms: {mol.num_node}")
print(f"Bonds: {mol.num_edge}")
print(f"Node feature dim: {mol.node_feature.shape}")  # (N_atoms, feature_dim)
print(f"Edge feature dim: {mol.edge_feature.shape}")  # (N_bonds*2, feature_dim)

# Convert a MoleculeNet / custom SMILES list to a dataset
from torchdrug import data as td_data
import pandas as pd

df = pd.read_csv("compounds.csv")   # columns: smiles, label
molecules = [td_data.Molecule.from_smiles(s) for s in df["smiles"] if s]
print(f"Loaded {len(molecules)} valid molecules")

# Check feature dimensions
print(f"Default atom feature dim: {molecules[0].node_feature.shape[1]}")

Module 2: GNN Architectures

TorchDrug provides GIN, RGCN, GraphSAGE, GAT, MPNN, AttentiveFP, and more.

from torchdrug import models, datasets

dataset = datasets.ESOL("~/data/esol", node_feature="default", edge_feature="default")
feature_dim = dataset.node_feature_dim

# Graph Isomorphism Network (GIN) — good default for property prediction
gin = models.GIN(
    input_dim=feature_dim,
    hidden_dims=[256, 256, 256],
    short_cut=True,
    batch_norm=True,
    concat_hidden=True,      # concatenate layer representations
)
print(f"GIN output_dim: {gin.output_dim}")

from torchdrug import models

# Message Passing Neural Network (MPNN) — captures edge features
mpnn = models.MPNN(
    input_dim=feature_dim,
    hidden_dim=256,
    edge_input_dim=16,       # edge feature dimension
    num_layer=4,
    num_gru_layer=1,
)

# Graph Attention Network (GAT) — attention-weighted neighbors
gat = models.GAT(
    input_dim=feature_dim,
    hidden_dims=[256, 256],
    edge_input_dim=16,
    num_head=8,
    batch_norm=True,
)
print(f"MPNN output_dim: {mpnn.output_dim}, GAT output_dim: {gat.output_dim}")

Module 3: Molecular Property Prediction

Wrap a GNN encoder with a prediction head for classification or regression.

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

# Regression example: ESOL aqueous solubility
dataset = datasets.ESOL("~/data/esol", node_feature="default", edge_feature="default")
train, val, test = dataset.split()
print(f"Train: {len(train)}, Val: {len(val)}, Test: {len(test)}")

model = models.GIN(
    input_dim=dataset.node_feature_dim,
    hidden_dims=[300, 300],
    short_cut=True,
    batch_norm=True,
    concat_hidden=True,
)

task = tasks.PropertyPrediction(
    model,
    task=dataset.tasks,       # list of property names
    criterion="mse",          # "mse" for regression, "bce" for classification
    metric=("mae", "rmse"),
    num_mlp_layer=2,
)

optimizer = torch.optim.Adam(task.parameters(), lr=1e-3, weight_decay=1e-5)
solver    = core.Engine(task, train, val, test, optimizer,
                        batch_size=32, log_interval=50)
solver.train(num_epoch=100)

# Evaluate on test set
metrics = solver.evaluate("test")
print(f"Test RMSE: {metrics['rmse']:.4f}")
print(f"Test MAE:  {metrics['mae']:.4f}")

Module 4: Drug-Target Interaction (DTI) Prediction

Predict binding affinity between molecules and protein sequences.

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

# Load a DTI dataset (e.g., Davis kinase binding affinities)
dataset = datasets.Davis("~/data/davis",
                          mol_node_feature="default",
                          mol_edge_feature="default")
train, val, test = dataset.split()

# Molecule encoder
mol_model = models.GIN(
    input_dim=dataset.mol_node_feature_dim,
    hidden_dims=[256, 256],
    short_cut=True,
    batch_norm=True,
    concat_hidden=True,
)

# Protein encoder (CNN on sequence)
prot_model = models.ProteinCNN(
    input_dim=21,            # amino acid vocabulary size
    hidden_dims=[128, 128, 128],
    kernel_size=3,
)

task = tasks.InteractionPrediction(
    mol_model, prot_model,
    task=dataset.tasks,
    criterion="mse",
    metric=("rmse", "pearsonr"),
)

optimizer = torch.optim.Adam(task.parameters(), lr=1e-3)
solver    = core.Engine(task, train, val, test, optimizer,
                        batch_size=64, log_interval=100)
solver.train(num_epoch=50)

metrics = solver.evaluate("test")
print(f"DTI Test RMSE: {metrics['rmse']:.4f}")
print(f"DTI Pearson r: {metrics['pearsonr']:.4f}")

Module 5: Retrosynthesis Prediction

Predict one-step retrosynthetic disconnections to find plausible building blocks.

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

# USPTO-50k retrosynthesis benchmark
dataset = datasets.USPTO50k("~/data/uspto50k",
                             as_synthon=False,
                             atom_feature=