Variational Bayesian Multi-Kernel Adaptive Deep Fusion for Microbe-Related Drug Prediction

Exploring potential microbe-drug associations (MDAs) not only facilitates drug discovery and clinical treatment but also contributes to a deeper understanding of microbial mechanisms. However, most MDA discoveries rely on biological experiments, which are time-consuming and costly. Therefore, developing an effective computational model to predict novel MDAs is of great importance. In this study, we propose a Variational Bayesian Multi-Kernel Adaptive Deep Fusion (VBMKADF) model for MDA prediction. We first integrate multiomics data to construct drug molecular graphs and a microbe hypergraph. Then, we perform multilayer graph convolution and hypergraph convolution to extract multilevel similarities of drugs and microbes, respectively. An attention mechanism is subsequently introduced to adaptively fuse these multilevel similarities, which are then incorporated into the Bayesian logistic matrix factorization framework to guide the generation of latent variable distributions. Additionally, we develop a variational Expectation-Maximization algorithm for adaptive inference of model hyperparameters and latent variables, which also guides the training of the deep learning model. Experimental results on two benchmark data sets across three scenarios show that, compared to other state-of-the-art methods, VBMKADF achieves higher AUPR, AUC, and F1 scores in both balanced and highly imbalanced settings. Moreover, case studies further confirm that VBMKADF can serve as an effective tool for MDA prediction.