A Machine Learning Model for Predicting Enantioselectivity in Hypervalent Iodine(III)Catalyzed Asymmetric Phenolic Dearomatizations  

作　　者：Ben Gao Liu Cai Yuchen Zhang Huaihai Huang Yao Li Xiao-Song Xue 

机构地区：[1]Key Laboratory of Fluorine and Nitrogen Chemistry and Advanced Materials,Shanghai Institute of Organic Chemistry,University of Chinese Academy of Sciences,Chinese Academy of Sciences,Shanghai 200032 [2]School of Chemistry and Materials Science,Hangzhou Institute for Advanced Study,University of Chinese Academy of Sciences,Hangzhou 310024

出　　处：《CCS Chemistry》2024年第10期2515-2528,共14页中国化学会会刊（英文）

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);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).

摘　　要：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.

关 键 词：machine learning chiral hypervalent iodine CATALYSIS DEAROMATIZATION ENANTIOSELECTIVITY 

分 类 号：O64[理学—物理化学]