Neighborhood Topology-Aware Knowledge Graph Learning and Microbial Preference Inferring for Drug-Microbe Association Prediction

作者：Jing Gu, Tiangang Zhang, Yihang Gao, Sentao Chen, Yuxin Zhang, Hui Cui, Ping Xuan · 发表于：Journal of Chemical Information and Modeling · 年份：2025 · DOI：10.1021/acs.jcim.4c01544 · 被引用次数：6 · 研究领域：Machine Learning in Bioinformatics、Bioinformatics and Genomic Networks、Tuberculosis Research and Epidemiology

The human microbiota may influence the effectiveness of drug therapy by activating or inactivating the pharmacological properties of drugs. Computational methods have demonstrated their ability to screen reliable microbe-drug associations and uncover the mechanism by which drugs exert their functions. However, the previous prediction methods failed to completely exploit the neighborhood topologies of the microbe and drug entities and the diverse correlations between the microbe-drug entity pair and the other entities. In addition, they ignored the case that a microbe prefers to associate with its own specific drugs. A novel prediction method, PCMDA, was proposed by learning the neighborhood topologies of entities, inferring the association preferences, and integrating the features of each entity pair based on multiple biological premises. First, a knowledge graph consisting of microbe, disease, and drug entities is established to help the subsequent integration of the topological structure of entities and the similarity, interaction, and association relationship between any two entities. We generate various topological embeddings for each microbe (or drug) entity through random walks with neighborhood restarts on the microbe-disease-drug knowledge graph. Distance-level attention is designed to adaptively fuse neighborhood topologies covering multiple ranges. Second, the topological embeddings of entities imply the latent topological relationships between entities, while the r...