Computational models, databases and tools for antibiotic combinations. Issue 5 (1st August 2022)

• Record Type: Journal Article
• Authors: Lv, Ji; Liu, Guixia; Hao, Junli; Ju, Yuan; Sun, Binwen; Sun, Ying
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 5(2022)
• Publication Date: 2022-08-01
• Subjects: antibiotic combinations -- machine learning -- complex network -- synergy effect -- databases; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbac309
• Languages: English
• ISSNs: 1467-5463

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.