Utilizing Molecular Descriptors in Graph Convolutional Neural Networks for Small Molecule Drug Discovery

Abstract

Small molecule drug discovery is a widely adopted therapeutic approach for treating a diverse range of diseases. This study presents and compares three methods for utilizing graph convolutional neural networks (GCNNs) to predict hits targeting CAG repeat DNA, by representing compounds as graph-based structures and incorporating molecular descriptors. The results demonstrate that combining graph structural information with molecular descriptors enhances predictive accuracy, even in relatively small datasets. We hope this report serves as a reference for improving the predictive accuracy of small molecule activity using GCNNs.