用简单引导搜索让旧算法解决起始原料受限合成问题
Tango*: Constrained synthesis planning using chemically informed value functions
- 用化学感知价值函数引导单向搜索，适配起始物料约束
- 优化单一超参数后，求解率与效率超越现有方法
- 适合需从废弃物或可再生原料出发的合成规划场景
计算机辅助合成规划（CASP）在无约束条件下生成简单分子逆合成路径方面取得显著进展。近期工作引入专用双向搜索算法，通过正向与逆向扩展解决起始原料受限的合成问题，使CASP系统能基于指定起始原料（如废料或可再生原料）提供合成路径。本文提出一种简易引导搜索方法，使现有单向搜索算法Retro*能够解决起始原料受限的合成规划问题。通过优化单一超参数，Tango*在效率和求解率上优于现有方法。我们发现Tango*的成本函数显著提升双向DESP方法的表现。该方法在生成路径长度相近的前提下，实现更低的墙钟时间。最后，我们分析了Tango*相较于神经引导搜索方法表现优异的潜在原因。
原文摘要 · Abstract (English)
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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