Computational models, databases and tools for antibiotic combinations

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 com...

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Veröffentlicht in:Briefings in bioinformatics Jg. 23; H. 5
Hauptverfasser: Lv, Ji, Liu, Guixia, Hao, Junli, Ju, Yuan, Sun, Binwen, Sun, Ying
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Oxford University Press 20.09.2022
Oxford Publishing Limited (England)
Schlagworte:
Antibiotics
Antimicrobial resistance
Computer applications
Mathematical models
Software
complex network
databases
synergy effect
machine learning
antibiotic combinations
ISSN:1467-5463, 1477-4054, 1477-4054
Online-Zugang:Volltext
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Zusammenfassung: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.
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ISSN:1467-5463
1477-4054
1477-4054
DOI:10.1093/bib/bbac309