Abstract
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.
Highlights
Antibiotic resistance is a growing health crisis, and it is emerging globally (Author Anonymous, 2013; Zhabiz et al, 2014; Murray et al, 2022)
We found that synergistic antibiotic combinations tend to have smaller network proximity
Since we concentrated on the subtle differences among synergy, additive, and antagonism, all 91 pairwise combinations fall into three categories, according to the αscore (Supplementary Table S1)
Summary
Antibiotic resistance is a growing health crisis, and it is emerging globally (Author Anonymous, 2013; Zhabiz et al, 2014; Murray et al, 2022) This crisis has been ascribed to the wide use and even abuse of antibiotics in the clinic, as well as a lack of economic incentives and market regulation of new antibiotic development (Ventola, 2015; Farha et al, 2021). With the development of artificial intelligence, many researchers have started to use computational approaches to identify synergistic drug combinations (Sheng et al, 2017; Weinstein et al, 2017). They used drug structures (Mason et al, 2017; Mason et al, 2018) and chemo-genomics
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
