期刊
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
卷 -, 期 -, 页码 -出版社
WILEY-V C H VERLAG GMBH
DOI: 10.1002/anie.202216160
关键词
Hiyama; Machine Learning; Nitrils; Palladium; Phosphane Ligands
Palladium-catalyzed couplings of silicon enolates with aryl electrophiles are limited to expensive bromide substrates. However, a study using ylide functionalized phosphines (YPhos) showed promising results in coupling inexpensive aryl chlorides with alpha-trimethylsilyl alkylnitriles. A statistical model was developed to predict catalyst structures with superior performance, leading to the synthesis of pharmaceutically significant compounds.
Palladium-catalyzed couplings of silicon enolates with aryl electrophiles are of great synthetic utility, but often limited to expensive bromide substrates. A comparative experimental study confirmed that none of the established ligand systems allows to couple inexpensive aryl chlorides with alpha-trimethylsilyl alkylnitriles. In contrast, ylide functionalized phosphines (YPhos) led to encouraging results. A statistical model was developed that correlates the reaction yields with ligand features. It was employed to predict catalyst structures with superior performance. With this cheminformatics approach, YPhos ligands were tailored specifically to the demands of Hiyama couplings. The newly synthesized ligands displayed record-setting activities, enabling the elusive coupling of aryl chlorides with alpha-trimethylsilyl alkyl nitriles. The preparative utility of the catalyst system was demonstrated by the synthesis of pharmaceutically meaningful alpha-aryl alkylnitriles, alpha-arylcarbonyls and biaryls.
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