Retrosynthesis is the task of planning a series of chemical reactions to create a desired molecule from simpler, buyable molecules. While previous works have proposed algorithms to find optimal solutions for a range of metrics (e.g. shortest, lowest-cost), these works generally overlook the fact that we have imperfect knowledge of the space of possible reactions, meaning plans created by algorithms may not work in a laboratory. In this paper we propose a novel formulation of retrosynthesis in terms of stochastic processes to account for this uncertainty. We then propose a novel greedy algorithm called retro-fallback which maximizes the probability that at least one synthesis plan can be executed in the lab. Using in-silico benchmarks we demonstrate that retro-fallback generally produces better sets of synthesis plans than the popular MCTS and retro* algorithms.

翻译：逆向合成是指规划一系列化学反应，从更简单、可购得的分子出发，合成目标分子。尽管已有研究提出了针对多种指标（如最短路径、最低成本）寻找最优解的算法，但这些工作通常忽略了我们对可能反应空间的知识不完善这一事实，即算法生成的方案可能无法在实验室中实现。本文提出了一种基于随机过程的新颖逆向合成形式化方法，以应对这种不确定性。随后，我们提出了一种名为“回溯落点法”（retro-fallback）的新颖贪心算法，该算法旨在最大化至少一条合成方案能在实验室中成功执行的概率。通过计算机模拟基准测试，我们证明了回溯落点法通常能生成比流行的MCTS和retro*算法更优的合成方案集。