Need help with dgl-lifesci?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.

About the developer

302 Stars 78 Forks Apache License 2.0 204 Commits 9 Opened issues


Python package for graph neural networks in chemistry and biology

Services available


Need anything else?

Contributors list


Documentation | Discussion Forum

We also have a slack channel for real-time discussion. If you want to join the channel, contact [email protected]


Deep learning on graphs has been an arising trend in the past few years. There are a lot of graphs in life science such as molecular graphs and biological networks, making it an import area for applying deep learning on graphs. DGL-LifeSci is a DGL-based package for various applications in life science with graph neural networks.

We provide various functionalities, including but not limited to methods for graph construction, featurization, and evaluation, model architectures, training scripts and pre-trained models.

For a list of community contributors, see here.

For a full list of work implemented in DGL-LifeSci, see here.



DGL-LifeSci should work on

  • all Linux distributions no earlier than Ubuntu 16.04
  • macOS X
  • Windows 10

It is recommended to create a conda environment for DGL-LifeSci with for example

conda create -n dgllife python=3.6

DGL-LifeSci requires python 3.6+, DGL 0.7.0+ and PyTorch 1.5.0+.

Install pytorch

Install dgl

Additionally, we require

RDKit 2018.09.3
for utils related to cheminformatics. We recommend installing it with
conda install -c rdkit rdkit==2018.09.3

For other installation recipes for RDKit, see the official documentation.

Pip installation for DGL-LifeSci

pip install dgllife

Installation from source

If you want to try experimental features, you can install from source as follows:

git clone
cd dgl-lifesci/python
python install

Verifying successful installation

Once you have installed the package, you can verify the success of installation with

import dgllife




If you use DGL-LifeSci in a scientific publication, we would appreciate citations to the following paper:

    title={DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life Science},
    author={Mufei Li and Jinjing Zhou and Jiajing Hu and Wenxuan Fan and Yangkang Zhang and Yaxin Gu and George Karypis},
    journal = {ACS Omega}

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.