ChemRxiv
These are preliminary reports that have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information. For more information, please see our FAQs.
1/1
2 files

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

preprint
submitted on 12.04.2020 and posted on 13.04.2020 by Krupal P. Jethava, Jonathan A Fine, Yingqi Chen, Ahad Hossain, Gaurav Chopra
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.

Funding

NIH NCATS ASPIRE Awards

Department of Chemistry start-up funds

Integrative Data Science Initiative award

NCATS Clinical and Translational Sciences Award from the Indiana Clinical and Translational Sciences Institute (UL1TR002529)

Purdue University Center for Cancer Research, NIH grant P30 CA023168

History

Email Address of Submitting Author

gchopra@purdue.edu

Institution

Purdue University - Center for Cancer Research

Country

United States

ORCID For Submitting Author

0000-0003-0942-7898

Declaration of Conflict of Interest

The authors declare no conflict of interest.

Version Notes

version 1 (manuscript, supporting)

Licence

Exports