小分子药物发现中的人工智能:对方法,应用和现实世界结果的批判性审查
1College of Pharmacy, University of Illinois, Chicago, IL 60612, USA.
Pharmaceuticals (Basel, Switzerland)
|September 27, 2025
概括
人工智能 (AI) 通过增强传统方法来增强小分子药物发现. 虽然人工智能在加速时间表和识别候选药物方面表现有前途,但临床成功仍然需要仔细验证和现实的期望.
科学领域:
- 制药科学 制药科学
- 计算化学计算化学
- 生物技术是生物技术.
背景情况:
- 人工智能 (AI) 越来越多地集成到药物发现中.
- 人工智能为传统方法提供了补充方法.
- 进化从基于规则的系统延伸到先进的深度学习和代理AI.
研究的目的:
- 审查AI在小分子药物发现中的演变和应用.
- 对AI在药物开发中的成功和失败提出平衡的观点.
- 为整合人工智能进入药物发现工作流程提供见解.
主要方法:
- 审查人工智能方法,包括深度学习,生成模型,扩散模型和代理人工智能.
- 在目标识别,击中发现,领先优化和安全预测中分析AI应用.
- 对著名的人工智能辅助药物发现项目的案例研究分析 (例如,巴里西提尼布,哈利,DSP-1181,ISM001-055).
主要成果:
- 人工智能已经为药物重用 (巴里西提尼布) 和临床前发现 (哈利) 作出了贡献.
- 几种人工智能辅助化合物面临临临床开发挑战 (例如,DSP-1181停止生产).
- 人工智能加速了发现时间表,但不能保证临床成功.
结论:
- 人工智能是小分子药物发现的宝贵补充工具.
- 在数据质量,可解释性,监管和伦理方面的挑战仍然存在.
- 混合人类-人工智能方法和代理人工智能系统是未来药物发现的关键.
关键词:
在ADMET预测预测.代理人工智能的人工智能人工智能的人工智能是人工智能.深度学习是一种深度学习.扩散模型的扩散模型发现药物的发现.基础模型 基础模型机器学习是机器学习.分子设计分子设计.虚拟选 虚拟选 虚拟选更多相关视频
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