Yönden Zafer, Reshadi Samira, Hayati Ahmad Farrokh, Hooshiar Mohammad Hossein, Ghasemi Sholeh, Yönden Hakan, Daemi Amin
Department of Medical Biochemistry, Faculty of Medicine, Cukurova University, Adana, Turkey.
School of Dentistry, Mashhad University of Medical Sciences, Mashhad, Iran.
Drug Dev Res. 2025 Feb;86(1):e70066. doi: 10.1002/ddr.70066.
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.
耐药细菌的出现，常被称为“超级细菌”，对全球卫生系统构成了深远且不断升级的挑战，超越了传统抗生素发现方法的能力范围。随着耐药机制迅速演变，对创新解决方案的需求从未如此迫切。本综述深入探讨了人工智能驱动方法在抗生素开发中的变革性作用，特别是针对耐药菌株（DRSBs），重点在于理解其作用机制。人工智能算法通过高效收集、分析和对复杂数据集建模，彻底改变了抗生素发现过程，以预测潜在抗生素的有效性和细菌耐药机制。这些计算技术进步使研究人员能够识别具有独特机制的有前景的抗生素候选物，这些机制能有效绕过传统耐药途径。通过专门针对关键细菌过程或破坏必需的细胞成分，这些由人工智能设计的抗生素为对抗即使是最具抗性的细菌菌株提供了有力解决方案。人工智能在抗生素设计中的应用代表了一种范式转变，能够快速精确地识别具有定制作用机制的新型化合物。这种方法不仅加速了药物开发时间表，还提高了针对超级细菌的精准度，显著改善了治疗效果。此外，了解这些由人工智能设计的抗生素的潜在机制对于优化其临床疗效以及制定预防进一步耐药出现的积极策略至关重要。人工智能驱动的抗生素发现有望在全球抗击抗菌药物耐药性的斗争中发挥关键作用。通过利用人工智能的力量，研究人员正在开辟有效治疗方法开发的新前沿，确保对耐药细菌日益增长的威胁做出积极且可持续的应对。
