基于蛋白质检测和抑制AMR途径的人工智能
Suchandrima Sadhukhan1, Rupsa Bhattacharya1, Debasmita Bhattcharya2
1Department of Biotechnology, Institute of Engineering and Management, Kolkata, University of Engineering and Management, Kolkata, India.
Journal of computer-aided molecular design
|November 25, 2025
概括
人工智能 (AI) 为打击抗微生物耐药性 (AMR) 提供了强大的工具. 本综述详细介绍了用于早期AMR检测,蛋白质分析和新型抑制剂设计的AI框架,以对抗抗性途径.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 抗微生物耐药性 (AMR) 构成了全球健康的重大威胁.
- 有效的抗菌耐药性监测需要高通量和精确的方法.
- 现有的策略往往缺乏及时干预所需的速度和准确性.
研究的目的:
- 提供人工智能 (AI) 框架对抗微生物耐药性 (AMR) 监测的全面概述.
- 在早期AMR检测,蛋白质表征和抑制剂设计中突出显示AI应用.
- 探索AI在识别抗药机制和开发新型治疗策略方面的作用.
主要方法:
- 深度学习算法 (例如DeepGO,GraphSite) 用于蛋白质功能注释.
- 用人工智能引导的蛋白质建模 (例如AlphaFold,ESMFold) 用于3D结构生成.
- 分子对接和动力学模拟 (例如,AutoDock,DeepDriveMD) 用于抑制剂分析.
- 用于抗性基因识别的元基因组分析工具 (例如DeepARG).
- 自然语言处理 (NLP) 和大型语言模型 (LLM) 用于文献挖掘和抑制器设计.
- 以人工智能为基础的蛋白质-蛋白质相互作用预测器,以了解抵抗路径.
主要成果:
- 人工智能框架能够精确地对抗性相关蛋白质进行功能性注释.
- 为分子对接和模拟生成高分辨率的蛋白质3D结构.
- 人工智能工具有助于识别耐药性基因和生物标志物.
- NLP和LLM有助于发现耐药性决定因素和设计抑制剂.
- 人工智能模型预测蛋白质-蛋白质相互作用,以指导新型抗生素的开发.
结论:
- 人工智能为抗微生物耐药性 (AMR) 监测和控制提供了一种变革性的方法.
- 人工智能驱动的工具提高了AMR的早期检测,表征和抑制.
- 不同的人工智能方法的整合有望加速对抗耐药病原体的新诊断和治疗方法的开发.
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