Rxn-INSIGHT:利用键-电子矩阵进行快速化学反应分析。

IF 7.1 2区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Maarten R. Dobbelaere, István Lengyel, Christian V. Stevens, Kevin M. Van Geem
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引用次数: 0

摘要

设计有机合成途径的挑战仍然是药物化学领域的核心问题。六十年来,计算机辅助合成规划催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:在获得一组反应物时,确定适当的溶剂、催化剂和试剂,以便在合成过程的特定步骤中获得并优化所需的产物。通常情况下,化学家会确定在反应中心产生关键影响的关键官能团和环,将反应分类,并为它们命名。本研究介绍了基于键-电子矩阵方法的开源算法 Rxn-INSIGHT,目的是实现这项工作的自动化。Rxn-INSIGHT 不仅简化了这一过程,还便于对反应数据库进行广泛查询,有效复制了有机化学家的思维过程。该算法的核心功能包括对反应进行分类和命名,从涉及的化学实体中提取官能团、环和支架。根据反应的相似性和普遍性提供反应条件建议最终成为附带应用。我们根据精心策划的基准数据集对基于规则的模型的性能进行了严格评估,结果显示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,选择类似反应的关键因素在于分析参与反应的环状结构。对美国专利商标局化学反应数据库中的环状结构进行检查后发现,仅用 35 个独特的环,就可以涵盖近 100 万种产品中所有环的 75%。此外,Rxn-INSIGHT 还能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建议,所有这一切都只需要一秒钟的时间,而且只需要一台日常使用的笔记本电脑。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rxn-INSIGHT: fast chemical reaction analysis using bond-electron matrices

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.

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来源期刊
Journal of Cheminformatics
Journal of Cheminformatics CHEMISTRY, MULTIDISCIPLINARY-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
14.10
自引率
7.00%
发文量
82
审稿时长
3 months
期刊介绍: Journal of Cheminformatics is an open access journal publishing original peer-reviewed research in all aspects of cheminformatics and molecular modelling. Coverage includes, but is not limited to: chemical information systems, software and databases, and molecular modelling, chemical structure representations and their use in structure, substructure, and similarity searching of chemical substance and chemical reaction databases, computer and molecular graphics, computer-aided molecular design, expert systems, QSAR, and data mining techniques.
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