Effects of Pooling Operations on Prediction of Ligand Rotation-Dependent Protein-Ligand Binding in 3D Graph Convolutional Network

Abstract

3D graph convolutional network (3DGCN) is a graph neural network (GNN) variant that utilizes the 3D bond information on molecules for chemical tasks. In 3DGCN, the pooling operation extracts the molecular features from the atomic features for molecule embedding and dimensionality reduction. In this work, we investigated the pooling effects on the performance of 3DGCN in the classification of protein-ligand binding events, especially when a ligand is rotated with respect to a target protein in the 3D Cartesian coordinate. 3DGCN showed the general ability for recognizing the ligand rotation, and its prediction accuracy was found to be pooling-dependent. For example, 3DGCN with max pooling did not recognize the rotations of known active ligands for human beta-secretase 1 faithfully, compared with the other pooling operations (sum, avg, and set2set). This work would contribute to augmented architecture evolution of 3DGCN for the chemical tasks that require the 3D molecular information including chirality.