Amine‐Promoted Asymmetric (4+2) Annulations for the Enantioselective Synthesis of Tetrahydropyridines: A Traceless and Recoverable Auxiliary Strategy

Hu, Pengfei; Hu, Jian; Jiao, Jiajun; Tong, Xiaofeng

doi:10.1002/ange.201300526

“…14 Notably, the use sulfamidate 15 , a cyclic N-sulfonylimine, has been used to prepare interesting heterocyclic scaffolds. Sulfamidate is transformed into a fused heterocycle using a Michael addition 16 , cycloaddition [17][18][19][20][21][22] , arylation [23][24][25] , alkenylation [26][27][28] , or alkynylation 26 strategy by leveraging electrophilicity of cyclic N-sulfonylimines (Scheme 1).…”

Section: Introductionmentioning

confidence: 99%

Accelerated Reactivity Mechanism and Interpretable Machine Learning Model of N-Sulfonylimines Towards Fast Multicomponent Reactions

Jethava¹,

Fine²,

Chen³

et al. 2020

Preprint

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Predicting the outcome of chemical reactions using machine learning models has emerged as a promising research area in chemical science. However, the use of such models to prospectively test new reactions by interpreting chemical reactivity is limited. We have developed a new fast and one-pot multicomponent reaction of N-sulfonylimines with heterogenous reactivity. Fast reaction times (<5 min) for both acyclic and cyclic sulfonylimine encouraged us to investigate plausible reaction mechanisms using quantum mechanics to identify intermediates and transition states. The heterogeneous reactivity of N-sulfonylimine lead us to develop a human-interpretable machine learning model using positive and negative reaction profiles. We introduce chemical reactivity flowcharts to help chemists interpret the decisions made by the machine learning model for understanding heterogeneous reactivity of N-sulfonylimines. The model learns chemical patterns to accurately predict the reactivity of N-sulfonylimine with different carboxylic acids and can be used to suggest new reactions to elucidate the substrate scope of the reaction. We believe our human-interpretable machine learning approach is a general strategy that is useful to understand chemical reactivity of components for any multicomponent reaction to enhance synthesis of drug-like libraries.

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“…14 Notably, the use sulfamidate 15 , a cyclic N-sulfonylimine, has been used to prepare interesting heterocyclic scaffolds. Sulfamidate is transformed into a fused heterocycle using a Michael addition 16 , cycloaddition [17][18][19][20][21][22] , arylation [23][24][25] , alkenylation [26][27][28] , or alkynylation 26 strategy by leveraging electrophilicity of cyclic N-sulfonylimines (Scheme 1). Scheme 1: Strategy to explore N-sulfonylimine reactivity towards multicomponent reaction However, among reported synthetic strategies, construction of direct C-C bond between the imine carbon and the (het)aromatic partner is underrepresented in the literature.…”

Section: Introductionmentioning

confidence: 99%

Accelerated Reactivity Mechanism and Interpretable Machine Learning Model of N-Sulfonylimines Towards Fast Multicomponent Reactions

Jethava¹,

Fine²,

Chen³

et al. 2020

Preprint

View full text Add to dashboard Cite

Predicting the outcome of chemical reactions using machine learning models has emerged as a promising research area in chemical science. However, the use of such models to prospectively test new reactions by interpreting chemical reactivity is limited. We have developed a new fast and one-pot multicomponent reaction of N-sulfonylimines with heterogenous reactivity. Fast reaction times (<5 min) for both acyclic and cyclic sulfonylimine encouraged us to investigate plausible reaction mechanisms using quantum mechanics to identify intermediates and transition states. The heterogeneous reactivity of N-sulfonylimine lead us to develop a human-interpretable machine learning model using positive and negative reaction profiles. We introduce chemical reactivity flowcharts to help chemists interpret the decisions made by the machine learning model for understanding heterogeneous reactivity of N-sulfonylimines. The model learns chemical patterns to accurately predict the reactivity of N-sulfonylimine with different carboxylic acids and can be used to suggest new reactions to elucidate the substrate scope of the reaction. We believe our human-interpretable machine learning approach is a general strategy that is useful to understand chemical reactivity of components for any multicomponent reaction to enhance synthesis of drug-like libraries.

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Allenes in Multicomponent Synthesis of Heterocycles

Reißig

¹

,

Zimmer

²

2014

Multicomponent Reactions in Organic Synthesis

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Amine‐Promoted Asymmetric (4+2) Annulations for the Enantioselective Synthesis of Tetrahydropyridines: A Traceless and Recoverable Auxiliary Strategy

Cited by 22 publications

References 62 publications

Accelerated Reactivity Mechanism and Interpretable Machine Learning Model of N-Sulfonylimines Towards Fast Multicomponent Reactions

Accelerated Reactivity Mechanism and Interpretable Machine Learning Model of N-Sulfonylimines Towards Fast Multicomponent Reactions

Accelerated Reactivity Mechanism and Interpretable Machine Learning Model of N-Sulfonylimines Towards Fast Multicomponent Reactions

Allenes in Multicomponent Synthesis of Heterocycles

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