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Review
. 2021 Sep;26(9):2138-2151.
doi: 10.1016/j.drudis.2021.04.016. Epub 2021 Apr 20.

Computational resources in the management of antibiotic resistance: Speeding up drug discovery

Lubna Maryam  1 , Salman Sadullah Usmani  1 , Gajendra P S Raghava  2
Affiliations
Review

Computational resources in the management of antibiotic resistance: Speeding up drug discovery

Lubna Maryam et al. Drug Discov Today. 2021 Sep.

Abstract

This article reviews more than 50 computational resources developed in past two decades for forecasting of antibiotic resistance (AR)-associated mutations, genes and genomes. More than 30 databases have been developed for AR-associated information, but only a fraction of them are updated regularly. A large number of methods have been developed to find AR genes, mutations and genomes, with most of them based on similarity-search tools such as BLAST and HMMER. In addition, methods have been developed to predict the inhibition potential of antibiotics against a bacterial strain from the whole-genome data of bacteria. This review also discuss computational resources that can be used to manage the treatment of AR-associated diseases.

Keywords: Antibiotic resistance; Computational biology; Databases; In silico tools.

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