Application of Molecular Graph Convolutional Neural Network: Prediction of Protein-Ligand Activity and Biosynthesis Pathways.

Abstract

Recently, machine learning based approaches by extracting physical features from molecular structures have been applied to classify molecular structural similarity, or prediction of biochemical activities such as ligand activity against target proteins in various applications. In this study, we will introduce an application that more efficiently trains molecular feature extraction using molecular graph convolution neural network (MGCNN), which is an application of deep learning model to chemical molecules.