Computer-aided synthesis planning (CASP) algorithms have demonstrated expert-level abilities in planning retrosynthetic routes to molecules of low to moderate complexity. However, current search methods assume the sufficiency of reaching arbitrary building blocks, failing to address the common real-world constraint where using specific molecules is desired. To this end, we present a formulation of synthesis planning with starting material constraints. Under this formulation, we propose Double-Ended Synthesis Planning (DESP), a novel CASP algorithm under a bidirectional graph search scheme that interleaves expansions from the target and from the goal starting materials to ensure constraint satisfiability. The search algorithm is guided by a goal-conditioned cost network learned offline from a partially observed hypergraph of valid chemical reactions. We demonstrate the utility of DESP in improving solve rates and reducing the number of search expansions by biasing synthesis planning towards expert goals on multiple new benchmarks. DESP can make use of existing one-step retrosynthesis models, and we anticipate its performance to scale as these one-step model capabilities improve.

翻译：计算机辅助合成规划（CASP）算法已在规划低至中等复杂度分子的逆合成路线方面展现出专家级能力。然而，现有搜索方法默认仅需抵达任意起始原料即可，未能处理实际应用中常需使用特定分子的约束条件。为此，我们提出了包含起始原料约束的合成规划问题框架。基于此框架，我们提出双端合成规划（DESP）——一种在双向图搜索框架下的新型CASP算法，通过交替扩展目标分子与目标起始原料来确保约束可满足性。该搜索算法由离线训练的、基于部分观测有效化学反应超图的目标条件成本网络进行引导。我们在多个新基准测试中证明，DESP通过将合成规划偏向专家设定的目标，能有效提升求解率并减少搜索扩展次数。DESP可兼容现有的一步逆合成模型，我们预期其性能将随着这些单步模型能力的提升而持续增强。