坦戈:为生成分子设计直接优化受约束的合成能力
Jeff Guo1,2, Philippe Schwaller3,4
1École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland. jeff.guo@epfl.ch.
Nature computational science
|March 3, 2026
概括
设计易于合成的分子仍然是一个挑战. 我们引入了Tanimoto组重叠 (TANGO) 奖励函数,以改善生成分子设计,并通过强化学习 (RL) 优化受约束合成能力.
科学领域:
- 计算化学是一种计算化学.
- 人工智能在药物发现中的作用
- 化学合成 化学合成
背景情况:
- 生成性分子设计在确保合成性方面面临着挑战.
- 分子的多参数优化往往忽视了实际的合成约束.
- 有效的分子重新利用,可持续性和合成效率至关重要.
研究的目的:
- 为了应对在生成分子设计中受限合成能力的挑战.
- 为优化分子合成开发一种新的奖励函数.
- 通过强化学习将可合成性纳入生成模型.
主要方法:
- 坦尼莫托集团重叠 (TANGO) 奖励函数的介绍.
- 用化学原理将二进制奖励转化为连续奖励.
- 增强分子生成模型与TANGO用于强化学习 (RL) 的增强.
- 设计用于处理各种合成约束的框架 (起始材料,中间,分离).
主要成果:
- 在生成模型中,TANGO可以直接优化受约束的合成能力.
- 提出的方法成功地将合成计划与分子设计相结合.
- 在导航复杂的合成优化场景中表现出有效性.
- 基于RL的激励对于挑战合成性问题来说是富有成效的.
结论:
- 坦戈奖励函数为优化分子合成能力提供了一种新的方法.
- 使用TANGO的强化学习增强了实际化学设计的生成模型.
- 该框架提供了一个整体解决方案,用于整合各种合成约束.
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