Tango*：使用化学信息价值函数的约束合成规划
Tango*: Constrained synthesis planning using chemically informed value functions
AI总结：
本文提出Tango*，通过化学信息价值函数和现有Retro*单向搜索解决起始原料受限的合成规划问题，在求解率、效率、耗时及路线长度上优于现有方法。
摘要：
计算机辅助合成规划（CASP）在以非约束方式为简单分子生成逆合成路径方面已取得显著进展。近期工作引入了一种具有正向和逆向扩展的专用双向搜索算法，以解决起始原料受限的合成问题，使CASP系统能够从指定起始原料（如废弃物或可再生原料）提供合成路径。在本工作中，我们引入了一种简单的引导式搜索，可利用现有的单向搜索算法Retro*求解起始原料受限的合成规划问题。我们表明，通过优化单个超参数，Tango*在效率和求解率方面优于现有方法。我们发现，Tango*代价函数为双向DESP方法带来了显著提升。我们的方法还实现了更低的挂钟时间，同时提出长度相近的合成路线，而路线长度是衡量路线质量的常用指标。最后，我们强调了Tango相较于神经引导搜索方法表现强劲的潜在原因。
英文摘要：
Computer-aided synthesis planning (CASP) has made significant strides in generating retrosynthetic pathways for simple molecules in a non-constrained fashion. Recent work introduces a specialised bidirectional search algorithm with forward and retro expansion to address the starting material-constrained synthesis problem, allowing CASP systems to provide synthesis pathways from specified starting materials, such as waste products or renewable feed-stocks. In this work, we introduce a simple guided search which allows solving the starting material-constrained synthesis planning problem using an existing, uni-directional search algorithm, Retro*. We show that by optimising a single hyperparameter, Tango* outperforms existing methods in terms of efficiency and solve rate. We find the Tango* cost function catalyses strong improvements for the bidirectional DESP methods. Our method also achieves lower wall clock times while proposing synthetic routes of similar length, a common metric for route quality. Finally, we highlight potential reasons for the strong performance of Tango over neural guided search methods
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