Prediction of Synergistic Antibiotic Combinations by Graph Learning

Frontiers in Pharmacology

Ying Sun²  Guixia Liu³  Ji Lv³  Yuan Ju⁴  Weiying Guo⁵ 

[1] College of Computer Science and Technology, Jilin University, Changchun, China;Department of Respiratory Medicine, The First Hospital of Jilin University, Changchun, China;Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China;Sichuan University Library, Sichuan University, Chengdu, China;The First Hospital of Jilin University, Changchun, China;

关键词: antibiotic combination;    antimicrobial resistance;    graph learning;    bacterial infection;    synergy effect;   

DOI  :  10.3389/fphar.2022.849006

来源: DOAJ

【 摘 要 】

Antibiotic resistance is a major public health concern. Antibiotic combinations, offering better efficacy at lower doses, are a useful way to handle this problem. However, it is difficult for us to find effective antibiotic combinations in the vast chemical space. Herein, we propose a graph learning framework to predict synergistic antibiotic combinations. In this model, a network proximity method combined with network propagation was used to quantify the relationships of drug pairs, and we found that synergistic antibiotic combinations tend to have smaller network proximity. Therefore, network proximity can be used for building an affinity matrix. Subsequently, the affinity matrix was fed into a graph regularization model to predict potential synergistic antibiotic combinations. Compared with existing methods, our model shows a better performance in the prediction of synergistic antibiotic combinations and interpretability.

【 授权许可】

Unknown