Machine Learning Prediction of Structure-Performance Relationship in Organic Synthesis

Data-driven approach has emerged as a powerful strategy in the construction of structure-performance relationships in organic synthesis.To close the gap between mechanistic understanding and synthetic prediction,we have made efforts to implement mechanistic knowledge in machine learning modelling of organic transformation,as a way to achieve accurate predictions of reactivity,regio-and stereoselectivity.We have constructed a comprehensive and balanced computational database for target radical transformations(arene C—H functionalization and HAT reaction),which laid the foundation for the reactivity and selectivity prediction.Furthermore,we found that the combination of computational statistics and physical organic descriptors offers a practical solution to build machine learning structure-performance models for reactivity and regioselectivity.To allow machine learning modelling of stereoselectivity,a structured database of asymmetric hydrogenation of olefins was built,and we designed a chemical heuristics-based hierarchical learning approach to effectively use the big data in the early stage of catalysis screening.Our studies reflect a tiny portion of the exciting developments of machine learning in organic chemistry.The synergy between mechanistic knowledge and machine learning will continue to generate a strong momentum to push the limit of reaction performance prediction in organic chemistry.

Enantioselective Intramolecular Desymmetric ct-Addition of Cyclohexa- none to Propiolamide Catalyzed by Sodium L-Prolinate

Summary of main observation and conclusion An enantioselective desymmetric nucleophilic α-addition of cyclohexanone to propiolamide has been developed through a 6-exo-dig cyclization reaction.By employing simple and readily available L-proline sodium salt as a bifunctional catalyst,a series of chiral 6,6-bicyclic bridged products bearing morphan scaffold have been isolated in good yields and excellent enantioselectivities.Density functional theory (DFT)calculations elucidated the origins of the enantioselectivity and regioselectivity of this transformation.A salt bridge that links the amide carbonyl group with proline carboxylate in the transition state was proven to be the driving force for the induction of excellent enantioselectivity.

An Unconventional trans-exo-Selective Cyclization of Alkyne-Tethered Cyclohexadienones Initiated by Rhodium(III)-Catalyzed C-H Activation via Insertion Relay

Different from the established trans-endo-selective cyclization of alkyne-tethered electrophiles that involve an E/Z isomerization process,herein,the authors present a novel strategy to allow trans-exo-selective arylative cyclization of 1,6-enynes.Through initiation of rhodium(III)-catalyzed C-H activation,a diverse range of N-heterocyclic directing groups,including pyridine,pyrazole,imidazo[1,2-a]pyridine,benzoxazole,benzothiazole,and purine,was feasible for the cascade transformation,exhibiting high efficiency(up to 92%yield),broad substrate scope,and excellent functional group compatibility.Moreover,the modification of natural products and pharmaceutical compounds was also demonstrated to showcase its synthetic utility.Based on density functional theory(DFT)calculations,a key three-membered ring intermediate through the insertion relay,rather than the direct E/Z isomerization of alkenyl rhodium species,controlled the stereochemical outcome for this trans-exo-selective cyclization.The subsequent ring-opening protonation of the more favored rotamer led to exclusive trans-exo-selectivity.