Learning Universal and Robust 3D Molecular Representations with Graph Convolutional Networks

TL;DR

This paper introduces a universal, robust 3D molecular descriptor called DNP and a graph convolutional network, RoM-GCN, that effectively integrates chemical and geometric features for improved molecular representations.

Contribution

The work proposes a new invariant and injective 3D geometric descriptor, DNP, and a graph neural network, RoM-GCN, that jointly utilize chemical and geometric features for molecular modeling.

Findings

DNP descriptor outperforms previous geometric descriptors in robustness.

RoM-GCN achieves superior results on protein and small molecule datasets.

Model demonstrates the importance of combining chemical and geometric features.

Abstract

To learn accurate representations of molecules, it is essential to consider both chemical and geometric features. To encode geometric information, many descriptors have been proposed in constrained circumstances for specific types of molecules and do not have the properties to be ``robust": 1. Invariant to rotations and translations; 2. Injective when embedding molecular structures. In this work, we propose a universal and robust Directional Node Pair (DNP) descriptor based on the graph representations of 3D molecules. Our DNP descriptor is robust compared to previous ones and can be applied to multiple molecular types. To combine the DNP descriptor and chemical features in molecules, we construct the Robust Molecular Graph Convolutional Network (RoM-GCN) which is capable to take both node and edge features into consideration when generating molecule representations. We evaluate our model on protein and small molecule datasets. Our results validate the superiority of the DNP descriptor in incorporating 3D geometric information of molecules. RoM-GCN outperforms all compared baselines.