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

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


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.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Animals
  • Anti-Bacterial Agents / therapeutic use
  • Computational Biology
  • Databases, Factual
  • Drug Discovery*
  • Drug Resistance, Bacterial*
  • Humans


  • Anti-Bacterial Agents