探戈:使用化学信息的价值函数进行受约束的合成规划
Daniel Armstrong1, Zlatko Jončev1, Jeff Guo1,2
1Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland daniel.armstrong@epfl.ch philippe.schwaller@epfl.ch.
Digital discovery
|August 18, 2025
概括
一种新的指导搜索方法,Tango*,有效地解决了初始材料受限的合成规划问题. 通过优化单个超参数,Tango*改进了现有的计算机辅助合成规划 (CASP) 方法.
科学领域:
- 计算化学的计算化学
- 有机合成 有机合成
- 化学中的人工智能.
背景情况:
- 计算机辅助合成规划 (CASP) 已经为不复杂的分子提供了先进的回合成路径生成.
- 开始材料受限合成规划的现有方法通常采用专门的双向搜索算法.
- 在有效地解决使用单向搜索的特定起始材料的合成规划问题方面存在差距.
研究的目的:
- 介绍一个新的指导式搜索算法,Tango*,用于初始材料受约束的合成规划问题.
- 为了证明Tango*可以有效地利用现有的单向搜索算法,如Retro*.
- 提高受约束的回合成分析的效率和成功率.
主要方法:
- 开发了一个简单的指导式搜索算法,名为Tango*.
- 将Tango*与单向搜索算法Retro*集成在一起.
- 在Tango*算法中优化单个超参数.
- 利用计算的节点成本函数来指导合成路径.
主要成果:
- 探戈*成功地解决了初始材料受约束的合成规划问题.
- 优化的Tango*算法与现有方法相比,显示出更高的效率.
- 在受约束的合成规划任务中,Tango*实现了更高的解决率.
- 节点成本函数有效地指导生成的合成路径.
结论:
- 坦哥*提供了一种高效和有效的解决方案,用于启动受材料限制的合成计划.
- 指导式搜索方法为专门的双向算法提供了有价值的替代方案.
- 进一步优化超参数和成本函数可以增强CASP的能力.
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