torchdrug

name	torchdrug
description	PyTorch-native graph neural networks for molecules and proteins. Use when building custom GNN architectures for drug discovery, protein modeling, or knowledge graph reasoning. Best for custom model development, protein property prediction, retrosynthesis. For pre-trained models and diverse featurizers use deepchem; for benchmark datasets use pytdc.
license	Apache-2.0 license
metadata	{"skill-author":"K-Dense Inc."}
verified	false
lastVerifiedAt	"2026-02-19T05:29:09.098Z"
source	builtin
trust_score	100
provenance_sha	a2a826bf005b6fba

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

uv pip install torchdrug
# Or with optional dependencies
uv pip install torchdrug[full]

Quick Example

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=,
    metric=[, ]
)


optimizer = torch.optim.Adam(task.parameters(), lr=)
train_loader = DataLoader(train_set, batch_size=, shuffle=)

 epoch  ():
     batch  train_loader:
        loss = task(batch)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()