Identification of HIV Inhibitors Using Graph Neural Networks

Abstract

Graph neural networks (GNN), primed to extract knowledge and discover patterns in graph-structured data, have received particularly increased attention in biomedical research. By integrating information from a variety of biomedical knowledge repositories they offer a fast and efficient computational alternative approach to the costly and time-consuming process of drug development and research. The core contributions of this paper include the design and empirical evaluation of several GNN-based models for the identification of potential HIV (Human Immunodeficiency Virus) inhibitors. In particular, the predictive power of model variants based on Graph Attention Network (GAT), Graph Isomorphism Network (GIN), and Continuous Kernel-Based Graph Convolutional Network, specifically developed to handle molecular data, have been investigated. To assess the effectiveness of the proposed models, the Stanford open graph benchmark dataset for molecular data ogbg-molhiv was used. Furthermore, two types of molecular fingerprints have been proposed to augment the molecular representation in the proposed graph neural models, leading to better performance standing compared to the original models. The paper provides a detailed description of the proposed models for identifying HIV inhibitors, followed by a comparative analysis of the experimental results focusing on a discussion of the challenges we face and future research directions that could be investigated.