Predicting reactants from a specified core product stands as a fundamental
challenge within organic synthesis, termed retrosynthesis prediction. Recently,
semi-template-based methods and graph-edits-based methods have achieved good
performance in terms of both interpretability and accuracy. However, due to
their mechanisms these methods cannot predict complex reactions, e.g.,
reactions with multiple reaction center or attaching the same leaving group to
more than one atom. In this study we propose a semi-template-based method, the
\textbf{Retro}synthesis via \textbf{S}earch \textbf{i}n (Hyper) \textbf{G}raph
(RetroSiG) framework to alleviate these limitations. In the proposed method, we
turn the reaction center identification and the leaving group completion tasks
as tasks of searching in the product molecular graph and leaving group
hypergraph respectively. As a semi-template-based method RetroSiG has several
advantages. First, RetroSiG is able to handle the complex reactions mentioned
above by its novel search mechanism. Second, RetroSiG naturally exploits the
hypergraph to model the implicit dependencies between leaving groups. Third,
RetroSiG makes full use of the prior, i.e., one-hop constraint. It reduces the
search space and enhances overall performance. Comprehensive experiments
demonstrated that RetroSiG achieved competitive results. Furthermore, we
conducted experiments to show the capability of RetroSiG in predicting complex
reactions. Ablation experiments verified the efficacy of specific elements,
such as the one-hop constraint and the leaving group hypergraph.

该研究提出了一种基于半模板的方法，即 RetroSiG 框架，用于预测复杂反应，并通过反应中心的识别和离去基团的完善任务来搜索产物分子图和离去基团超图，以此来缓解现有方法中的限制。

基于 (超) 图搜索的逆向合成预测

Retrosynthesis Prediction via Search in (Hyper) Graph

Retrosynthesis is the process of determining the set of reactant molecules
that can react to form a desired product. Semi-template-based retrosynthesis
methods, which imitate the reverse logic of synthesis reactions, first predict
the reaction centers in the products, and then complete the resulting synthons
back into reactants. These methods enable necessary interpretability and high
practical utility to inform synthesis planning. We develop a new offline-online
reinforcement learning method RLSynC for synthon completion in
semi-template-based methods. RLSynC assigns one agent to each synthon, all of
which complete the synthons by conducting actions step by step in a
synchronized fashion. RLSynC learns the policy from both offline training
episodes and online interactions which allow RLSynC to explore new reaction
spaces. RLSynC uses a forward synthesis model to evaluate the likelihood of the
predicted reactants in synthesizing a product, and thus guides the action
search. We compare RLSynC with the state-of-the-art retrosynthesis methods. Our
experimental results demonstrate that RLSynC can outperform these methods with
improvement as high as 14.9% on synthon completion, and 14.0% on
retrosynthesis, highlighting its potential in synthesis planning.

基于模板的半模板合成方法 RLSynC 通过完成合成子来指导合成规划，结果表明 RLSynC 在合成规划中的潜力较高。