人工智能设计的耐药超级细菌抗生素及其作用机制综述

IF 3.5 4区 医学 Q2 CHEMISTRY, MEDICINAL
Zafer Yönden, Samira Reshadi, Ahmad Farrokh Hayati, Mohammad Hossein Hooshiar, Sholeh Ghasemi, Hakan Yönden, Amin Daemi
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引用次数: 0

摘要

耐药细菌的出现,通常被称为“超级细菌”,对全球卫生系统构成了深刻和不断升级的挑战,超越了传统抗生素发现方法的能力。随着抵抗机制的迅速发展,对创新解决方案的需求从未像现在这样迫切。这篇综述深入探讨了人工智能驱动的方法在抗生素开发中的变革作用,特别是在靶向耐药细菌菌株(DRSBs)方面,重点是了解它们的作用机制。人工智能算法通过有效地收集、分析和建模复杂的数据集来预测潜在抗生素的有效性和细菌耐药性的机制,彻底改变了抗生素的发现过程。这些计算上的进步使研究人员能够识别出有希望的候选抗生素,这些抗生素具有独特的机制,可以有效地绕过传统的耐药途径。通过专门针对关键的细菌过程或破坏基本的细胞成分,这些人工智能设计的抗生素为对抗最具弹性的细菌菌株提供了强有力的解决方案。人工智能在抗生素设计中的应用代表了一种范式转变,能够快速准确地识别具有定制作用机制的新化合物。这种方法不仅加快了药物开发时间,而且提高了靶向超级细菌的精度,显著改善了治疗效果。此外,了解这些人工智能设计的抗生素的潜在机制对于优化其临床疗效和制定预防进一步耐药性出现的主动策略至关重要。人工智能驱动的抗生素发现将在全球抗击抗菌素耐药性的斗争中发挥关键作用。通过利用人工智能的力量,研究人员正在开发有效治疗方法的新领域,确保对耐药细菌日益增长的威胁做出积极和可持续的反应。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reviewing on AI-Designed Antibiotic Targeting Drug-Resistant Superbugs by Emphasizing Mechanisms of Action

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.

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来源期刊
CiteScore
6.40
自引率
2.60%
发文量
104
审稿时长
6-12 weeks
期刊介绍: Drug Development Research focuses on research topics related to the discovery and development of new therapeutic entities. The journal publishes original research articles on medicinal chemistry, pharmacology, biotechnology and biopharmaceuticals, toxicology, and drug delivery, formulation, and pharmacokinetics. The journal welcomes manuscripts on new compounds and technologies in all areas focused on human therapeutics, as well as global management, health care policy, and regulatory issues involving the drug discovery and development process. In addition to full-length articles, Drug Development Research publishes Brief Reports on important and timely new research findings, as well as in-depth review articles. The journal also features periodic special thematic issues devoted to specific compound classes, new technologies, and broad aspects of drug discovery and development.
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