BANNMDA: a Computational Model for Predicting Potential Microbe-Drug Associations Based on Bilinear Attention Networks and Nuclear Norm Minimization

Mingmin Liang, Xianzhi Liu,Juncai Li, Qijia Chen,Bin Zeng,Zhong Wang, Jing Li,Lei Wang

FRONTIERS IN MICROBIOLOGY（2025）

Hunan Vocat Coll Sci & Technol | Hunan Vocat Coll Elect & Technol | Changsha Univ

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Abstract

IntroductionPredicting 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(BAN) with the Nuclear Norm Minimization (NNM) to uncover hidden connections between microbes and drugs.MethodsIn 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.Results and discussionThe experimental results indicated that BANNMDA outperformed state-of-the-art competitive methods. 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.

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Key words

computational model,microbe-drug associations,bilinear attention networks,nuclear norm minimization,prediction

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【要点】：论文提出了BANNMDA计算模型，通过结合双线性注意力网络和核范数最小化方法，预测微生物与药物间的潜在关联。

【方法】：作者构建了异质微生物-药物网络，整合了多种药物和微生物相似性度量以及已知的微生物-药物关系，并应用双线性注意力网络和核范数最小化来计算潜在微生物-药物关联的预测分数。

【实验】：研究通过5折交叉验证框架评估了BANNMDA的预测性能，使用的数据集未明确提及，实验结果显示BANNMDA优于现有先进方法，并在案例研究中对已知药物和病原体的关联预测得到了文献支持的验证。