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

13 April 2020, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


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.


multicomponent reactions (MCRs)
oxadiazole derivatives
Machine Learning
Chemical Reactivity Flowchart
chemical synthesis methods
Chemical Reaction
New Reactions
New reaction mechanisms
density functional theory
Reaction mechanism
Decision Trees
Reactivity Prediction

Supplementary materials

chemRxiv--Jethava-Fine Imine Reactivity Supporting

Supplementary weblinks


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