Node-aware convolution in Graph Neural Networks for Predicting molecular properties

Abstract

Molecular property prediction is a challenging task which aims to solve various issues of science namely drug discovery, materials discovery. It focuses on understanding the structure-property relationship between atoms in a molecule. Previous approaches have to face difficulties dealing with the various structure of the molecule as well as heavy computational time. Our model, in particular, utilizes the idea of message passing neural network and Schnet on the molecular graph with enhancement by adding the Node-aware Convolution and Edge Update layer in order to acquire the local information of the graph and to propagate interaction between atoms. Through experiments, our model has been shown the outperformance with previous deep learning methods in predicting quantum mechanical, calculated molecular properties in the QM9 dataset and magnetic interaction of two atoms in molecules approaches.