Classification of biosynthesis pathways of alkaloids using Graph Convolutional Networks

Abstract

Various chemical descriptors such like molecular fingerprints have been long discussed to represent biochemical features, in order to embed molecular structures into a numerical space and quantify their activities. However, it is still difficult to predict bioactivities from molecular structures since it depends on the choices of those chemical descriptors. Recently, machine learning methods based on Graph Convolutional Neural Networks (GCNN) have been proposed that can automatically optimize a model for molecular feature extraction from the given training sets. In this study, we introduce an application of GCNN to predict metabolic pathways of alkaloids, namely, one of the largest families of secondary metabolites in plants. We trained and tested GCNN model on alkaloid compounds and the mean accuracy of 20 runs with random sampling is about 94% (Number of epoch: 200). The results showed that it is greatly expected that it will lead to an understanding of the evolution of metabolic system unique to organisms.