Standardizing Substrate Selection: A Strategy toward Unbiased Evaluation of Reaction Generality

IF 12.7 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY ACS Central Science Pub Date : 2024-04-08 DOI:10.1021/acscentsci.3c01638
Debanjan Rana, Philipp M. Pflüger, Niklas P. Hölter, Guangying Tan and Frank Glorius*, 
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Abstract

With over 10,000 new reaction protocols arising every year, only a handful of these procedures transition from academia to application. A major reason for this gap stems from the lack of comprehensive knowledge about a reaction’s scope, i.e., to which substrates the protocol can or cannot be applied. Even though chemists invest substantial effort to assess the scope of new protocols, the resulting scope tables involve significant biases, reducing their expressiveness. Herein we report a standardized substrate selection strategy designed to mitigate these biases and evaluate the applicability, as well as the limits, of any chemical reaction. Unsupervised learning is utilized to map the chemical space of industrially relevant molecules. Subsequently, potential substrate candidates are projected onto this universal map, enabling the selection of a structurally diverse set of substrates with optimal relevance and coverage. By testing our methodology on different chemical reactions, we were able to demonstrate its effectiveness in finding general reactivity trends by using a few highly representative examples. The developed methodology empowers chemists to showcase the unbiased applicability of novel methodologies, facilitating their practical applications. We hope that this work will trigger interdisciplinary discussions about biases in synthetic chemistry, leading to improved data quality.

We introduce an objective substrate scope selection method for assessing the generality of chemical reactions.

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底物选择标准化:对反应通用性进行无偏评估的策略
每年都有超过 10,000 个新的反应方案出现,但其中只有少数方案从学术界转化为应用。造成这种差距的一个主要原因是缺乏有关反应范围的全面知识,即该方案可以或不可以应用于哪些底物。尽管化学家们投入了大量精力来评估新方案的适用范围,但由此得出的适用范围表仍存在重大偏差,从而降低了其表现力。在此,我们报告了一种标准化底物选择策略,旨在减少这些偏差,并评估任何化学反应的适用性和限制。我们利用无监督学习来绘制工业相关分子的化学空间图。随后,潜在的候选底物被投射到这一通用图谱上,从而选择出具有最佳相关性和覆盖范围的结构多样的底物。通过在不同的化学反应中测试我们的方法,我们利用几个极具代表性的例子证明了该方法在发现一般反应趋势方面的有效性。所开发的方法使化学家能够展示新方法无偏见的适用性,从而促进其实际应用。我们希望这项工作能引发有关合成化学中偏差的跨学科讨论,从而提高数据质量。
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来源期刊
ACS Central Science
ACS Central Science Chemical Engineering-General Chemical Engineering
CiteScore
25.50
自引率
0.50%
发文量
194
审稿时长
10 weeks
期刊介绍: ACS Central Science publishes significant primary reports on research in chemistry and allied fields where chemical approaches are pivotal. As the first fully open-access journal by the American Chemical Society, it covers compelling and important contributions to the broad chemistry and scientific community. "Central science," a term popularized nearly 40 years ago, emphasizes chemistry's central role in connecting physical and life sciences, and fundamental sciences with applied disciplines like medicine and engineering. The journal focuses on exceptional quality articles, addressing advances in fundamental chemistry and interdisciplinary research.
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