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

翻译：计算机辅助合成规划（CASP）在非约束条件下为简单分子生成逆合成路径方面取得了显著进展。近期研究引入了一种专门的双向搜索算法，通过前向与逆向扩展来解决起始物料约束的合成问题，使CASP系统能够从特定起始物料（如废弃物或可再生原料）提供合成路径。本研究提出一种简单的引导搜索方法，可在现有单向搜索算法Retro*的基础上解决起始物料约束的合成规划问题。我们证明通过优化单个超参数，Tango*在效率与求解率方面均优于现有方法。研究发现Tango*的成本函数能显著提升双向DESP方法的性能。在提出合成路径长度（衡量路径质量的常用指标）相近的情况下，本方法同时实现了更低的实际计算时间。最后，我们探讨了Tango相对于神经引导搜索方法表现出优越性能的潜在原因。