Tehran University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share this content! On (X network) By
Reviewing on Ai-Designed Antibiotic Targeting Drug-Resistant Superbugs by Emphasizing Mechanisms of Action Publisher Pubmed



Yonden Z1 ; Reshadi S2 ; Hayati AF3 ; Hooshiar MH4 ; Ghasemi S5 ; Yonden H6 ; Daemi A1
Authors
Show Affiliations
Authors Affiliations
  1. 1. Department of Medical Biochemistry, Faculty of Medicine, Cukurova University, Adana, Turkey
  2. 2. School of Dentistry, Mashhad University of Medical Sciences, Mashhad, Iran
  3. 3. School of medicine, Islamic Azad University of Medical Sciences, Qeshm, Iran
  4. 4. Department of Periodontics, School of Dentistry, Tehran University of Medical Sciences, Tehran, Iran
  5. 5. Department of Internal Medicine, School of Medicine, Urmia Univesity of Medical Science, Urmia, Iran
  6. 6. Department of Health Services and Vocational School Management, Health Institution Management, Tarsus University, Tarsus, Turkey

Source: Drug Development Research Published:2025


Abstract

The emergence of drug-resistant bacteria, often referred to as “superbugs,” poses a profound and escalating challenge to global health systems, surpassing the capabilities of traditional antibiotic discovery methods. As resistance mechanisms evolve rapidly, the need for innovative solutions has never been more critical. This review delves into the transformative role of AI-driven methodologies in antibiotic development, particularly in targeting drug-resistant bacterial strains (DRSBs), with an emphasis on understanding their mechanisms of action. AI algorithms have revolutionized the antibiotic discovery process by efficiently collecting, analyzing, and modeling complex datasets to predict both the effectiveness of potential antibiotics and the mechanisms of bacterial resistance. These computational advancements enable researchers to identify promising antibiotic candidates with unique mechanisms that effectively bypass conventional resistance pathways. By specifically targeting critical bacterial processes or disrupting essential cellular components, these AI-designed antibiotics offer robust solutions for combating even the most resilient bacterial strains. The application of AI in antibiotic design represents a paradigm shift, enabling the rapid and precise identification of novel compounds with tailored mechanisms of action. This approach not only accelerates the drug development timeline but also enhances the precision of targeting superbugs, significantly improving therapeutic outcomes. Furthermore, understanding the underlying mechanisms of these AI-designed antibiotics is crucial for optimizing their clinical efficacy and devising proactive strategies to prevent the emergence of further resistance. AI-driven antibiotic discovery is poised to play a pivotal role in the global fight against antimicrobial resistance. By leveraging the power of artificial intelligence, researchers are opening new frontiers in the development of effective treatments, ensuring a proactive and sustainable response to the growing threat of drug-resistant bacteria. © 2025 Wiley Periodicals LLC.