Microbe-Drug Association Prediction Model Based on Adaptive Network Fusion of Structural-Topological Information With Integration Strategy

Currently, microbial drug resistance has become a serious global problem. Investigating the relationship between microbes and drugs can facilitate understanding of microbial resistance to certain drugs and help develop more effective therapeutic strategies. Therefore, an efficient and rapid computational method for identifying microbe-drug associations is very important for optimizing antimicrobial therapy, monitoring drug resistance, and addressing bacterial resistance challenges. In this paper, a model called ANFAISMDA for identifying potential microbe-drug associations is proposed. First, Structural information of microbes and drugs is obtained based on 16S rRNA gene sequences of microbes and SMILES structures of drugs, respectively. Topological information of microbes and drugs is obtained based on symmetric matrix completion algorithm, respectively. Then, the structural and topological information is effectively fused according to our proposed adaptive network fusion algorithm. The algorithm combines network node and path information while also adaptively updating weights based on inter-network transmissibility. Finally, an integration strategy is proposed to significantly improve model performance and thereby more accurately identify novel microbe-drug associations. Overall, the reliability and validity of the model are fully verified by experiments. Further, we also have visualized the top 50 microbe-drug associations and have found some interesting association patterns. The results of the study show the importance of our approach in optimizing antimicrobial therapy, monitoring drug resistance, and addressing the challenges of bacterial resistance. In addition, the study confirms the usefulness of the model in gaining a deeper understanding of the complex mechanisms of microbe-drug associations.