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- Title
Electrocatalyzed direct arene alkenylations without directing groups for selective late-stage drug diversification.
- Authors
Lin, Zhipeng; Dhawa, Uttam; Hou, Xiaoyan; Surke, Max; Yuan, Binbin; Li, Shu-Wen; Liou, Yan-Cheng; Johansson, Magnus J.; Xu, Li-Cheng; Chao, Chen-Hang; Hong, Xin; Ackermann, Lutz
- Abstract
Electrooxidation has emerged as an increasingly viable platform in molecular syntheses that can avoid stoichiometric chemical redox agents. Despite major progress in electrochemical C−H activations, these arene functionalizations generally require directing groups to enable the C−H activation. The installation and removal of these directing groups call for additional synthesis steps, which jeopardizes the inherent efficacy of the electrochemical C−H activation approach, leading to undesired waste with reduced step and atom economy. In sharp contrast, herein we present palladium-electrochemical C−H olefinations of simple arenes devoid of exogenous directing groups. The robust electrocatalysis protocol proved amenable to a wide range of both electron-rich and electron-deficient arenes under exceedingly mild reaction conditions, avoiding chemical oxidants. This study points to an interesting approach of two electrochemical transformations for the success of outstanding levels of position-selectivities in direct olefinations of electron-rich anisoles. A physical organic parameter-based machine learning model was developed to predict position-selectivity in electrochemical C−H olefinations. Furthermore, late-stage functionalizations set the stage for the direct C−H olefinations of structurally complex pharmaceutically relevant compounds, thereby avoiding protection and directing group manipulations. Electrochemistry has emerged as an increasingly viable tool in molecular synthesis. Here the authors realize electrocatalyzed C−H activations, with the aid of data science and artificial intelligence, towards selective alkenylations for late-stage drug diversifications.
- Subjects
MACHINE learning; ALKENYLATION; ARTIFICIAL intelligence; ELECTROCATALYSIS
- Publication
Nature Communications, 2023, Vol 14, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-023-39747-0