Catalytic asymmetric dearomatization(CADA)of phenols has emerged as a powerful strategy for constructing stereochemically complicated architectures from planar aromatic feedstocks.However,the development of novel cata...Catalytic asymmetric dearomatization(CADA)of phenols has emerged as a powerful strategy for constructing stereochemically complicated architectures from planar aromatic feedstocks.However,the development of novel catalysts for highly enantioselective phenolic oxidative dearomatization continues to be a time-and resource-intensive endeavor,attributable mainly to the paucity of a reliable predictive catalyst design strategy.In this study,we systematically compiled a dataset of 847 literaturereported asymmetric phenolic dearomatization by hypervalent iodine(III)catalysts(HVI-CADA dataset),a unique type of catalyst that is gaining increasing attention owing to their ecofriendly features.Leveraging this reaction dataset,we established a machine learning predictive model to predict enantioselectivity.The XGBoost algorithm exhibited the optimal performance,with a root-mean-square error of 0.26(kcal/mol)and an R^(2)of 0.84.This established model can effectively guide the selection of the optimal catalyst and additives in out-of-sample tests.Subsequent independent experiments were conducted to validate the results obtained from the model predictions.We anticipate that our current work will facilitate further design,optimization,and development of novel chiral hypervalent iodine catalysts for new asymmetric phenolic dearomatization reactions.展开更多
基金supported by the Ministry of Science and Technology of China(grant no.2021YFF0701700)the National Natural Science Foundation of China(grant nos.22122104,22193012,and 21933004)+1 种基金the CAS Project for Young Scientists in Basic Research(grant nos.YSBR-052 and YSBR-095)the Strategic Priority Research Program of the Chinese Academy of Sciences(grant no.XDB0590000).
文摘Catalytic asymmetric dearomatization(CADA)of phenols has emerged as a powerful strategy for constructing stereochemically complicated architectures from planar aromatic feedstocks.However,the development of novel catalysts for highly enantioselective phenolic oxidative dearomatization continues to be a time-and resource-intensive endeavor,attributable mainly to the paucity of a reliable predictive catalyst design strategy.In this study,we systematically compiled a dataset of 847 literaturereported asymmetric phenolic dearomatization by hypervalent iodine(III)catalysts(HVI-CADA dataset),a unique type of catalyst that is gaining increasing attention owing to their ecofriendly features.Leveraging this reaction dataset,we established a machine learning predictive model to predict enantioselectivity.The XGBoost algorithm exhibited the optimal performance,with a root-mean-square error of 0.26(kcal/mol)and an R^(2)of 0.84.This established model can effectively guide the selection of the optimal catalyst and additives in out-of-sample tests.Subsequent independent experiments were conducted to validate the results obtained from the model predictions.We anticipate that our current work will facilitate further design,optimization,and development of novel chiral hypervalent iodine catalysts for new asymmetric phenolic dearomatization reactions.
