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https://github.com/DeepGraphLearning/torchdrug

A powerful and flexible machine learning platform for drug discovery
https://github.com/DeepGraphLearning/torchdrug

deep-learning drug-discovery graph-neural-networks pytorch

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A powerful and flexible machine learning platform for drug discovery

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README 

        
[![TorchDrug](asset/torchdrug_logo_full.svg)](https://torchdrug.ai/)



  with

  

    TorchProtein

  




[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Tbnr1Fog_YjkqU1MOhcVLuxqZ4DC-c8-#forceEdit=true&sandboxMode=true)

[![Contributions](https://img.shields.io/badge/contributions-welcome-blue)](https://github.com/DeepGraphLearning/torchdrug/blob/master/CONTRIBUTING.md)

[![License Apache-2.0](https://img.shields.io/github/license/DeepGraphLearning/torchdrug?color=blue)](https://github.com/DeepGraphLearning/torchdrug/blob/master/LICENSE)

[![PyPI downloads](https://static.pepy.tech/personalized-badge/torchdrug?period=total&units=international_system&left_color=grey&right_color=blue&left_text=downloads)](https://pypi.org/project/torchdrug/)

[![TorchDrug Twitter](https://img.shields.io/twitter/url?label=TorchDrug&style=social&url=https%3A%2F%2Ftwitter.com%2FDrugTorch)](https://twitter.com/DrugTorch)



[Docs] | [Tutorials] | [Benchmarks] | [Papers Implemented]



[Docs]: https://deepgraphlearning.github.io/torchdrug-site/docs

[Tutorials]: https://deepgraphlearning.github.io/torchdrug-site/docs/tutorials

[Benchmarks]: https://deepgraphlearning.github.io/torchdrug-site/docs/benchmark

[Papers Implemented]: https://deepgraphlearning.github.io/torchdrug-site/docs/paper



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



[PyTorch]: https://pytorch.org/



Installation

------------



TorchDrug can be installed on either Linux, Windows or macOS. It is compatible with

3.7 <= Python <= 3.10 and PyTorch >= 1.8.0.



### From Conda ###



```bash

conda install torchdrug -c milagraph -c conda-forge -c pytorch -c pyg

```



### From Pip ###



```bash

pip install torch==1.9.0

pip install torch-scatter torch-cluster -f https://pytorch-geometric.com/whl/torch-1.9.0+cu102.html

pip 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 ###



```bash

git clone https://github.com/DeepGraphLearning/torchdrug

cd torchdrug

pip install -r requirements.txt

python setup.py install

```



### Windows (PowerShell) ###



We need to first install the build tools for Visual Studio. We then install the

following modules in PowerShell.



```powershell

Install-Module Pscx -AllowClobber

Install-Module VSSetup

```



Initialize Visual Studio in PowerShell with the following commands. We may setup

this for all PowerShell sessions by writing it to the PowerShell profile. Change

the library path according to your own case.



```powershell

Import-VisualStudioVars -Architecture x64

$env:LIB += ";C:\Program Files\Python37\libs"

```



### Apple Silicon (M1/M2 Chips) ###



We need PyTorch >= 1.13 to run TorchDrug on Apple silicon. For `torch-scatter` and

`torch-cluster`, they can be compiled from their sources. Note TorchDrug doesn't

support `mps` devices.



```bash

pip install torch==1.13.0

pip install git+https://github.com/rusty1s/pytorch_scatter.git

pip install git+https://github.com/rusty1s/pytorch_cluster.git

pip install torchdrug

```



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.



```python

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.



```python

mol = data.Molecule.from_smiles("CCOC(=O)N", atom_feature="default", bond_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.



```python

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.



```python

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)

```



```python

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.

```python

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])

```



Experiments can be easily tracked and managed through [Weights & Biases platform].

```python

solver = core.Engine(task, train_set, valid_set, test_set, optimizer, logger="wandb")

```



[Weights & Biases platform]: https://wandb.ai/



Contributing

------------



Everyone is welcome to contribute to the development of TorchDrug.

Please refer to [contributing guidelines](CONTRIBUTING.md) for more details.



License

-------



TorchDrug is released under [Apache-2.0 License](LICENSE).