The challenge of devising pathways for organic synthesis remains a central issue in the field of medicinal chemistry. Over the span of six decades, computer-aided synthesis planning has given rise to a plethora of potent tools for formulating synthetic routes. Nevertheless, a significant expert task still looms: determining the appropriate solvent, catalyst, and reagents when provided with a set of reactants to achieve and optimize the desired product for a specific step in the synthesis process. Typically, chemists identify key functional groups and rings that exert crucial influences at the reaction center, classify reactions into categories, and may assign them names. This research introduces Rxn-INSIGHT, an open-source algorithm based on the bond-electron matrix approach, with the purpose of automating this endeavor. Rxn-INSIGHT not only streamlines the process but also facilitates extensive querying of reaction databases, effectively replicating the thought processes of an organic chemist. The core functions of the algorithm encompass the classification and naming of reactions, extraction of functional groups, rings, and scaffolds from the involved chemical entities. The provision of reaction condition recommendations based on the similarity and prevalence of reactions eventually arises as a side application. The performance of our rule-based model has been rigorously assessed against a carefully curated benchmark dataset, exhibiting an accuracy rate exceeding 90% in reaction classification and surpassing 95% in reaction naming. Notably, it has been discerned that a pivotal factor in selecting analogous reactions lies in the analysis of ring structures participating in the reactions. An examination of ring structures within the USPTO chemical reaction database reveals that with just 35 unique rings, a remarkable 75% of all rings found in nearly 1 million products can be encompassed. Furthermore, Rxn-INSIGHT is proficient in suggesting appropriate choices for solvents, catalysts, and reagents in entirely novel reactions, all within the span of a second, utilizing nothing more than an everyday laptop.

中文翻译：

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Rxn-INSIGHT：使用键电子矩阵进行快速化学反应分析

设计有机合成途径的挑战仍然是药物化学领域的中心问题。在六十年的时间里，计算机辅助合成规划已经产生了大量用于制定合成路线的有效工具。尽管如此，一项重要的专家任务仍然迫在眉睫：在提供一组反应物时确定适当的溶剂、催化剂和试剂，以实现和优化合成过程中特定步骤所需的产物。通常，化学家会识别对反应中心产生重要影响的关键官能团和环，将反应分类，并为它们指定名称。本研究引入了 Rxn-INSIGHT，一种基于键电子矩阵方法的开源算法，目的是使这项工作自动化。 Rxn-INSIGHT 不仅简化了流程，还促进了反应数据库的广泛查询，有效地复制了有机化学家的思维过程。该算法的核心功能包括反应的分类和命名，从所涉及的化学实体中提取官能团、环和支架。基于反应的相似性和普遍性提供反应条件建议最终作为副应用出现。我们基于规则的模型的性能已经根据精心策划的基准数据集进行了严格评估，反应分类的准确率超过 90%，反应命名的准确率超过 95%。值得注意的是，人们已经认识到，选择类似反应的关键因素在于参与反应的环结构的分析。对 USPTO 化学反应数据库中的环结构进行的检查表明，只需 35 个独特的环，就可以涵盖近 100 万种产品中发现的所有环中的 75%。此外，Rxn-INSIGHT 能够熟练地为全新反应中的溶剂、催化剂和试剂提出适当的选择，所有这些都在一秒钟内完成，只需使用日常笔记本电脑即可。