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.
Wu, Zhenqin, et al. "MoleculeNet: a benchmark for molecular machine learning." Chemical science 9.2 (2018): 513-530.
informatics deep-learning, cheminformatics molecule, drug-discovery, geometric-deep-learning, graph-neural-networks,
Curator: laiwei.cool@gmail.com