Electrocatalyzed direct arene alkenylations without directing groups for selective late-stage drug diversification

• Published in: Nature communications Vol. 14; no. 1; p. 4224
• Main 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
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 15.07.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 119/118; 140/131; 140/58; 639/638/161/886; 639/638/403/933; Agents (artificial intelligence); Aromatic compounds; Artificial intelligence; Atom economy; Catalysis; Chemical synthesis; Chemistry; Electrocatalysis; Electrochemistry; Electrodes; Humanities and Social Sciences; Hydrocarbons; Ligands; Machine learning; multidisciplinary; Oxidants; Oxidation-Reduction; Oxidizing agents; Palladium; Palladium - chemistry; Science; Science (multidisciplinary)
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-023-39747-0

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.