人工智能指导药物的重新用途
Zhimin Fu1, Yuxin Yang2,3, Mina K Chung4,5,6
1Department of Pharmacy Services, University Hospitals of Cleveland, Cleveland, Ohio, USA.
Annual review of medicine
|January 27, 2026
概括
人工智能 (AI) 和机器学习 (ML) 可以通过分析大量的遗传和多组数据来加速药物重定向. 这些工具有助于确定有效的,负担得起的药物,用于挑战性疾病,推进精准医学.
科学领域:
- 药理学和计算生物学
- 遗传学和多组学 数据分析 数据分析
- 翻译医学是一种翻译医学.
背景情况:
- 药物重新定位为开发对挑战性疾病的治疗方法提供了一个快速的策略,利用现有的药物数据.
- 目前的药物重定向努力受到广泛的遗传和多组数据集未充分利用的限制.
- 在准确地应用先进的计算方法来探索这些丰富的生物数据集以获得新的治疗发现方面存在差距.
研究的目的:
- 批判性地审查人工智能 (AI) 和机器学习 (ML) 在药物重新利用中的应用.
- 探索AI/ML在识别新的,负担得起的重用药物的潜力和影响.
- 引导遗传学家,药理学家和计算科学家为人工智能驱动的药物发现做出贡献.
主要方法:
- 关于人工智能和机器学习在药物重用中的应用现有文献的综述.
- 讨论AI/ML与遗传学的整合,多组数据 (基因组学,转录组学,蛋白质组学,代谢组学,放射组学) 和电子健康记录.
- 在AI/ML的背景下,探索现实世界的数据收集和众包知识.
主要成果:
- 人工智能和机器学习可以有效地分析大规模数据集,以识别潜在的药物候选人进行重新利用.
- 这些技术有助于快速识别对挑战性疾病的有效治疗方法.
- 人工智能/ML方法对于理解临床上有意义的效果大小和对精准医学的影响至关重要.
结论:
- 人工智能和机器学习是药物重新利用的变革性工具,可以有效分析复杂的生物数据.
- 将AI/ML与多种数据源集成,可以加速发现廉价且易于获得的药物.
- 这些方法对将翻译医学结合起来,并为挑战人类疾病开发新型治疗方法具有重大前景.
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