Computational models, databases and tools for antibiotic combinations

Author:

Lv Ji12ORCID,Liu Guixia12,Hao Junli3,Ju Yuan4,Sun Binwen5ORCID,Sun Ying6

Affiliation:

1. College of Computer Science and Technology, Jilin University , Changchun, China

2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University , Changchun, China

3. College of Food Science, Northeast Agricultural University , Harbin, China

4. Sichuan University Library, Sichuan University , Chengdu, China

5. Engineering Research Center for New Materials and Precision Treatment Technology of Malignant Tumor Therapy, The Second Affiliated Hospital of Dalian Medical University , Dalian, China

6. Department of Respiratory Medicine, the First Hospital of Jilin University , Changchun, China

Abstract

Abstract Antibiotic combination is a promising strategy to extend the lifetime of antibiotics and thereby combat antimicrobial resistance. However, screening for new antibiotic combinations is both time-consuming and labor-intensive. In recent years, an increasing number of researchers have used computational models to predict effective antibiotic combinations. In this review, we summarized existing computational models for antibiotic combinations and discussed the limitations and challenges of these models in detail. In addition, we also collected and summarized available data resources and tools for antibiotic combinations. This study aims to help computational biologists design more accurate and interpretable computational models.

Funder

National Natural Science Foundation of China

Science and Technology Development Program of Jilin Province

Natural Science Foundation of Jilin Province

Publisher

Oxford University Press (OUP)

Subject

Molecular Biology,Information Systems

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