SHARP:通过基于碎片的层次行动空间增强学习来生成可合成的分子,以实现帕雷托优化
Jeonghyeon Kim1, Seongok Ryu2, Woohyeong Lee3
1Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea.
Journal of chemical information and modeling
|October 24, 2025
概括
可合成的层次行动空间 强化学习用于帕雷托优化 (SHARP) 通过优化结合亲和力和可合成性来生成药物分子. 这种人工智能方法克服了现有模型的局限性,为药物发现提供了高效和合理的分子设计.
科学领域:
- 药用化学 医学化学
- 计算化学计算化学
- 人工智能在药物发现中的作用
背景情况:
- 设计具有多种性质的分子,如高结合亲和力和合成性,是一个复杂的优化挑战.
- 由于优化反的局限性,当前的深度学习模型往往无法生成有效的,合成可行的分子.
研究的目的:
- 推出一种新的分子发生器,SHARP,解决AI驱动药物发现的局限性.
- 改进具有高亲和力和合成可访问性的类似药物分子的生成.
主要方法:
- 开发了用于帕雷托优化 (SHARP) 的可合成的等级行动空间强化学习,这是一个基于片段的等级强化学习模型.
- 整合了一种可合成性估计模型 (SEM) 用于行动掩盖,以确保合成可访问性.
- 采用复合奖励功能,包括对接分数,药物相匹配和溶剂可访问性,用于RL政策培训.
主要成果:
- 在四个优化任务中,SHARP在产生具有良好的合成能力的高亲和度分子方面始终优于现有方法.
- 该模型成功地解决了无效结构,局部最佳和合成不可行性的问题.
- 在碎片生长,链接设计,脚手架跳跃和侧链装饰方面证明有效.
结论:
- 用化学直观的动作空间进行强化学习是人工智能驱动药物发现的强大方法.
- 在基于结构的应用中,SHARP为合理的分子设计提供了一个强大的框架.
- 该方法增强了功能相关和实验可处理的候选药物的生成.
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