在小分子药物发现中,人工智能驱动的多药学
1Lankenau Institute for Medical Research, 100 E Lancaster Ave., Penn Wynne, PA 19096, USA.
International journal of molecular sciences
|July 29, 2025
概括
多药理学,为多个目标设计药物,提供了一种强大的策略,以打击药物耐药性和提高治疗效率. 人工智能正在加速对复杂疾病的这些先进的多目标疗法的发现.
科学领域:
- 药物的发现和开发.
- 计算化学是一种计算化学.
- 药理学 药理学是指药理学的学科.
背景情况:
- 多药理学,即针对多个治疗点的分子的设计,解决了诸如生物冗余和耐药性等挑战.
- 它通过潜在的协同效应,减少不良事件和改善患者遵守,比组合疗法具有优势.
研究的目的:
- 审查多药理学在各种疾病中的科学依据和应用.
- 探索计算方法和人工智能 (AI) 在加速多药学药物发现中的作用.
- 讨论omics数据和途径模拟在指导多目标药物设计中的整合.
主要方法:
- 关于多药理学的科学文献的综述.
- 探索计算方法,包括基于联体的建模,基于结构的对接,网络药理学和系统生物学.
- 对人工智能的最新进展进行分析,例如深度学习,强化学习和用于多目标代理发现的生成模型.
主要成果:
- 多药理学在瘤学,神经退行,代谢障碍和传染病方面取得了成功.
- 由人工智能驱动的平台正在使双重和多重目标化合物的新设计成为可能,其 in vitro 疗效已被证明.
- 整合omics数据和路径模拟有助于指导多目标设计.
结论:
- 人工智能支持的多药理学代表了复杂疾病药物发现的重大进步.
- 通过解决生物复杂性的问题,它有可能提供更有效的个性化疗法.
- 需要进行进一步的研究,以解决多药理学当前AI方法的挑战和局限性.
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