Role of Computer Aided Drug Design in Identifying New Genes Related to Antimicrobial Resistance

Authors

  • Kalpana Krishnaraju, M. Vijey Aanandhi

DOI:

https://doi.org/10.47750/pnr.2023.14.S01.186

Abstract

Antimicrobial resistance (AMR) poses challenges to healthcare systems around the world as drug-resistant pathogens emerge. The rapid spread of AMR could render current treatments ineffective or less effective, with devastating health and social consequences. Understanding the factors may facilitate therapeutic development of more effective AMR solutions. I have many other health problems. Current machine learning frameworks are so focused on known AMR genes that they often miss genes that are not yet involved in resistance. Moreover, these genes can create new resistance traits that contribute to the emergence of superbugs. So it's important to define them. Employing a machine learning framework that prioritizes genes thought to be involved in resistance to identify new resistances by analyzing the complete gene sets of different bacterial strains that are sensitive or resistant to a particular drug We identified the genes and analyzed the associated phenotypic traits. Furthermore, molecular docking studies and homology modeling of the proteins encoded by the prioritized genes show that these proteins and the antimicrobial agents to which the strains containing these proteins are known to be resistant. It shows that there is a significant correlation. Sustained interactions were evident. Our results highlight the potential of machine learning frameworks to identify genes not previously associated with antimicrobial resistance and may motivate additional research to combat AMR.

 

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Published

2023-02-15 — Updated on 2023-02-17

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How to Cite

Role of Computer Aided Drug Design in Identifying New Genes Related to Antimicrobial Resistance. (2023). Journal of Pharmaceutical Negative Results, 1367-1372. https://doi.org/10.47750/pnr.2023.14.S01.186 (Original work published 2023)