Mingmin Liang ¹ Xianzhi Liu ^1* Juncai Li ^1* Qijia Chen ^1* Bin Zeng ¹ Zhong Wang ¹ Jing Li ^1* Lei Wang ²

• ¹ Hunan vocational College of Electronic and Technology, Changsha, China
• ² Changsha University, Changsha, Hunan, China

Predicting potential associations between microbes and drugs is crucial for advancing pharmaceutical research and development. In this manuscript, we introduced an innovative computational model named BANNMDA by integrating Bilinear Attention Networks (BANs) with the Nuclear Norm Minimization (NNM) to uncover hidden connections between microbes and drugs. In BANNMDA, we initially constructed a heterogeneous microbe-drug network by combining multiple drug and microbe similarity metrics with known microbe-drug relationships. Subsequently, we applied both BAN and NNM to compute predicted scores of potential microbe-drug associations. Finally, we implemented 5-fold cross-validation frameworks to evaluate the prediction performance of BANNMDA, and experimental results indicated that BANNMDA outperformed state-of-the-art competitive methods. Additionally, we conducted case studies on well-known drugs such as the Amoxicillin and Ceftazidime, as well as on pathogens such as Bacillus cereus and Influenza A virus, to further evaluate the efficacy of BANNMDA, and experimental outcomes showed that there were 9 out of the top 10 predicted drugs, along with 8 and 9 out of the top 10 predicted microbes having been corroborated by relevant literatures. These findings underscored the capability of BANNMDA to achieve commendable predictive accuracy.