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

• Published in: ACS central science 2024-04, Vol.10 (4), p.899-906
• Main Authors: Rana, Debanjan, Pflüger, Philipp M., Hölter, Niklas P., Tan, Guangying, Glorius, Frank
• Format: Article
• Language: English
• Subjects: Algorithms; Approximation; Bias; Chemical reactions; Chemistry; Chemists; Clustering; Datasets; Libraries; Machine learning; Mapping; Pharmaceuticals; Protocol; Substrates; Unsupervised learning
• ISSN: 2374-7943; 2374-7951
• DOI: 10.1021/acscentsci.3c01638

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.