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TorchDrug is a PyTorch-based machine learning toolbox designed for several purposes.

• Easy implementation of graph operations in a PyTorchic style with GPU support
• Being friendly to practitioners with minimal knowledge about drug discovery
• Rapid prototyping of machine learning research

Installation

TorchDrug is compatible with Python 3.7/3.8 and PyTorch >= 1.4.0.

From Conda

conda install -c milagraph -c conda-forge torchdrug

From Pip

pip3 install torch==1.9.0
pip3 install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+cu102.html
pip3 install torchdrug

To install torch-scatter for other PyTorch or CUDA versions, please see the instructions in https://github.com/rusty1s/pytorch_scatter

From Source

git clone https://github.com/DeepGraphLearning/torchdrug
cd torchdrug
pip install -r requirements.txt
python setup.py install

Quick Start

TorchDrug is designed for humans and focused on graph structured data. It enables easy implementation of graph operations in machine learning models. All the operations in TorchDrug are backed by PyTorch framework, and support GPU acceleration and auto differentiation.

from torchdrug import data

edge_list = [[0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 0]]
graph = data.Graph(edge_list, num_node=6)
graph = graph.cuda()
# the subgraph induced by nodes 2, 3 & 4
subgraph = graph.subgraph([2, 3, 4])

Molecules are also supported in TorchDrug. You can get the desired molecule properties without any domain knowledge.

mol = data.Molecule.from_smiles("CCOC(=O)N", node_feature="default", edge_feature="default")
print(mol.node_feature)
print(mol.atom_type)
print(mol.to_scaffold())

You may also register custom node, edge or graph attributes. They will be automatically processed during indexing operations.

with mol.edge():
	mol.is_CC_bond = (mol.edge_list[:, :2] == td.CARBON).all(dim=-1)
sub_mol = mol.subgraph(mol.atom_type != td.NITROGEN)
print(sub_mol.is_CC_bond)

TorchDrug provides a wide range of common datasets and building blocks for drug discovery. With minimal code, you can apply standard models to solve your own problem.

import torch
from torchdrug import datasets

dataset = datasets.Tox21()
dataset[0].visualize()
lengths = [int(0.8 * len(dataset)), int(0.1 * len(dataset))]
lengths += [len(dataset) - sum(lengths)]
train_set, valid_set, test_set = torch.utils.data.random_split(dataset, lengths)

from torchdrug import models, tasks

model = models.GIN(dataset.node_feature_dim, hidden_dims=[256, 256, 256, 256])
task = tasks.PropertyPrediction(model, task=dataset.tasks)

Training and inference are accelerated by multiple CPUs or GPUs. This can be seamlessly switched in TorchDrug by just a line of code.

from torchdrug import core

# Single CPU / Multiple CPUs / Distributed CPUs
solver = core.Engine(task, train_set, valid_set, test_set, optimizer)
# Single GPU
solver = core.Engine(task, train_set, valid_set, test_set, optimizer, gpus=[0])
# Multiple GPUs
solver = core.Engine(task, train_set, valid_set, test_set, optimizer, gpus=[0, 1, 2, 3])
# Distributed GPUs
solver = core.Engine(task, train_set, valid_set, test_set, optimizer, gpus=[0, 1, 2, 3, 0, 1, 2, 3])

Contributing

Everyone is welcome to contribute to the development of TorchDrug. Please refer to contributing guidelines for more details.

License

TorchDrug is released under Apache-2.0 License.