Mutagenic Prediction for Chemical Compound Discovery with Partitioned Graph Convolution Network

Abstract

Aromatic compounds are organic matters that have the form of carbon rings, such as benzene rings. The compound is contained in most real-life chemicals, including medicines, detergents, and cosmetics. Because mutagenicity of these substances can pose a great health risk if they enter the human body, predicting mutagenicity and avoiding risk are important problems. Due to recent advances in deep learning, many studies have been conducted to predict the mutagenicity of graphically expressed aromatic compounds, and they are performing well. However, previous methods that used deep learning to predict the mutagenicity of molecules lead to the problem of dilution of local information in molecular structures. In graph neural networks, the embedding of graphs is determined by the average or sum of node embeddings, and when a particular node dominates, information from local nodes is diluted. In this paper, we propose a model that learns molecules' local information properties to be undiluted by separating them from the original graph. By partitioning the graph, we preserve local information by breaking the relationship between the dominant nodes and the non-dominant nodes so that they do not affect each other's information updates. Experiments show that we successfully partition carbon rings and functional groups in molecular graphs using the Girvan Newman algorithm, and embed the segmented graphs using the same neural networks, allowing neural networks to learn about carbon rings and functional groups. Comparisons with other neural networks confirm performance improvements on 1% accuracy.