MDGNN: Microbial Drug Prediction Based on Heterogeneous Multi-Attention Graph Neural Network - Pi, Jiangsheng; Jiao, Peishun; Zhang, Yang; Li, Junyi (Frontiers in Microbiology, 2022)
Human beings are now facing one of the largest public health crises in history with the outbreak of COVID-19. Traditional drug discovery could not keep peace with newly discovered infectious diseases. The prediction of drug-virus associations not only provides insight into the mechanism of drug-virus interactions, but also guides the screening of potential antiviral drugs. We develop a deep learning algorithm based on Graph Convolution Networks (MDGNN) to predict potential antiviral drugs. MDGNN is consisted of new node-level attention and feature-level attention mechanism, and shows its effectiveness compared with other comparative algorithms. MDGNN integrates the global information of the graph in the process of information aggregation by introducing the attention at node and feature level to graph convolution. Comparative experiments show that MDGNN achieves state-of-the-art performance with an AUC of 0.9721 and an AUPR of 0.9102. In the Case Study, two drugs related to SARS-COV-2 were successfully predicted and verified by relevant literature.