Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science.
#2 best model for Drug Discovery on QM9
In addition, we introduce a graph convolutional model that consistently matches or outperforms models using fixed molecular descriptors as well as previous graph neural architectures on both public and proprietary datasets.
Many applications of machine learning require a model to make accurate pre-dictions on test examples that are distributionally different from training ones, while task-specific labels are scarce during training.
In this paper, we propose a generalizable and transferable Multilevel Graph Convolutional neural Network (MGCN) for molecular property prediction.
#5 best model for Graph Regression on Lipophilicity