人工智能设计的分子用于药物发现,结构新性评估和影响
Shihan Xie1,2, Hui Zhu1,2, Niu Huang1,2
1Tsinghua Institute of Multidisciplinary Biomedical Research, Tsinghua University, Beijing 102206, China.
Journal of chemical information and modeling
|August 18, 2025
概括
人工智能 (AI) 在药物发现方面表现有前途,但通常会产生具有有限结构新奇性的分子. 基于结构的AI方法通常比基于联体的方法产生更多的新型化合物,这突显了需要仔细设计工作流程的需要.
科学领域:
- 药用化学 医学化学
- 计算化学的计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 实现结构性新性是现代药物发现的一个关键挑战.
- 人工智能 (AI) 擅长分析分子结构-活性关系,但其探索新化学空间的潜力尚未完全被理解.
- 评估人工智能产生的化合物的新性对于推进药物设计至关重要.
研究的目的:
- 在各种药物发现案例中系统评估人工智能设计的活性化合物的结构新性.
- 为了比较基于连接体和基于结构的AI方法产生的新性.
- 确定影响人工智能驱动药物发现工作流程新性的因素.
主要方法:
- 分析了71个已发表的AI设计活性化合物的案例.
- 使用相似度指标 (例如,塔尼莫托系数) 评估结构性新性.
- 基于连接体和基于结构的AI建模策略的结果比较.
主要成果:
- 基于干的AI模型经常产生具有低新奇性的分子 (58.1%的病例Tcmax>0.4).
- 基于结构的AI方法在产生新型化合物方面表现出卓越的性能 (17.9%的Tcmax>0.4).
- 选工作流程和目标特征显著影响AI产生的分子的新性.
结论:
- 系统的新性评估和手动验证对于防止AI药物发现中的结构同质化至关重要.
- 优化人工智能驱动的药物发现需要多样化的训练数据,跳意识到相似度指标,以及明智地使用相似度过器.
- 跨学科的合作对于平衡新化学结构的产生与所需的生物活动至关重要.
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