  Graph neural networks (GNNs) constitute a class of deep learning methods for
graph data. They have wide applications in chemistry and biology, such as
molecular property prediction, reaction prediction and drug-target interaction
prediction. Despite the interest, GNN-based modeling is challenging as it
requires graph data pre-processing and modeling in addition to programming and
deep learning. Here we present DGL-LifeSci, an open-source package for deep
learning on graphs in life science. DGL-LifeSci is a python toolkit based on
RDKit, PyTorch and Deep Graph Library (DGL). DGL-LifeSci allows GNN-based
modeling on custom datasets for molecular property prediction, reaction
prediction and molecule generation. With its command-line interfaces, users can
perform modeling without any background in programming and deep learning. We
test the command-line interfaces using standard benchmarks MoleculeNet, USPTO,
and ZINC. Compared with previous implementations, DGL-LifeSci achieves a speed
up by up to 6x. For modeling flexibility, DGL-LifeSci provides well-optimized
modules for various stages of the modeling pipeline. In addition, DGL-LifeSci
provides pre-trained models for reproducing the test experiment results and
applying models without training. The code is distributed under an Apache-2.0
License and is freely accessible at https://github.com/awslabs/dgl-lifesci.
