A Chronicle Review of In-Silico Approaches for Discovering Novel Antimicrobial Agents to Combat Antimicrobial Resistance
- PMID: 39282180
- PMCID: PMC11399514
- DOI: 10.1007/s12088-024-01355-x
A Chronicle Review of In-Silico Approaches for Discovering Novel Antimicrobial Agents to Combat Antimicrobial Resistance
Abstract
Antimicrobial resistance (AMR) poses a foremost threat to global health, necessitating innovative strategies for discovering antimicrobial agents. This review explores the role and recent advances of in-silico techniques in identifying novel antimicrobial agents and combating AMR giving few briefings of recent case studies of AMR. In-silico techniques, such as homology modeling, virtual screening, molecular docking, pharmacophore modeling, molecular dynamics simulation, density functional theory, integrated machine learning, and artificial intelligence, are systematically reviewed for their utility in discovering antimicrobial agents. These computational methods enable the rapid screening of large compound libraries, prediction of drug-target interactions, and optimization of drug candidates. The review discusses integrating in-silico approaches with traditional experimental methods and highlights their potential to accelerate the discovery of new antimicrobial agents. Furthermore, it emphasizes the significance of interdisciplinary collaboration and data-sharing initiatives in advancing antimicrobial research. Through a comprehensive discussion of the latest developments in in-silico techniques, this review provides valuable insights into the future of antimicrobial research and the fight against AMR.
Supplementary information: The online version contains supplementary material available at 10.1007/s12088-024-01355-x.
Keywords: Antimicrobial resistance (AMR); Artificial intelligence (AI); Machine learning (ML); Molecular docking; Molecular dynamic simulation (MDS); Virtual screening.
© Association of Microbiologists of India 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Conflict of interest statement
Conflict of interestsThe authors have no competing interests to declare that are relevant to the content of this article.
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References
-
- Priyanto JA, Prastya ME, Hening ENW et al (2024) Two strains of endophytic bacillus velezensis carrying antibiotic-biosynthetic genes show antibacterial and antibiofilm activities against methicillin-resistant Staphylococcus aureus (MRSA). Indian J Microbiol. 10.1007/s12088-024-01262-110.1007/s12088-024-01262-1 - DOI
-
- CDC (2022) The biggest antibiotic-resistant threats in the U.S. In: Centers for disease control and prevention. https://www.cdc.gov/drugresistance/biggest-threats.html. Accessed 25 Feb 2024
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