在药物发现和早期药物开发中整合人工智能:一种变革性的方法
Alberto Ocana1,2, Atanasio Pandiella3, Cristian Privat4
1Experimental Therapeutics in Cancer Unit, Medical Oncology Department, Instituto de Investigación Sanitaria San Carlos (IdISSC), Hospital Clínico San Carlos and CIBERONC, Madrid, Spain. alberto.ocana@salud.madrid.org.
Biomarker research
|March 15, 2025
概括
人工智能 (AI) 通过增强目标识别和分子设计来加速药物发现. 人工智能集成简化了临床开发,但需要强大的数据和协作来克服局限性.
科学领域:
- 计算生物学是一种计算生物学.
- 药理学 药理学 是一个学科.
- 生物信息学是一种生物信息学.
背景情况:
- 传统的药物发现是低效的,昂贵的,耗时的.
- 高失败率困扰着传统药物开发管道.
- 人工智能为这些固有的挑战提供了解决方案.
研究的目的:
- 提供AI整合在药物发现和开发中的概述.
- 突出AI在提高关键阶段的作用,从目标识别到临床试验.
- 讨论AI在制药研究中的潜力和局限性.
主要方法:
- 多种经济学数据分析和基于网络的目标识别方法.
- 人工智能模型 (例如AlphaFold) 用于蛋白质结构预测和药物适用性评估.
- 虚拟查和新的药物设计,以优化分子结构.
主要成果:
- 人工智能能识别出新的瘤性弱点和治疗目标.
- 精确的蛋白质结构预测有助于基于结构的药物设计.
- 人工智能优化了使用预测建模的患者招募和临床试验设计.
结论:
- 人工智能集成改变了药物发现和开发,加速了时间表.
- 解决数据偏差,概括性和道德问题对于AI实施至关重要.
- 合作和高质量的数据对于实现AI在加速药物开发方面的全部潜力至关重要.
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