Journal article
Krupal P. Jethava, Jonathan A Fine, Yingqi Chen, Ahad Hossain, G. Chopra
Organic Letters, 2020
Organic Letters, 2020
Semantic Scholar
DOI
PubMed
Cite
Cite
APA
Click to copy
Jethava, K. P., Fine, J. A., Chen, Y., Hossain, A., & Chopra, G. (2020). Accelerated Reactivity Mechanism and Interpretable Machine Learning Model of N-Sulfonylimines toward Fast Multicomponent Reactions. Organic Letters.
Chicago/Turabian
Click to copy
Jethava, Krupal P., Jonathan A Fine, Yingqi Chen, Ahad Hossain, and G. Chopra. “Accelerated Reactivity Mechanism and Interpretable Machine Learning Model of N-Sulfonylimines toward Fast Multicomponent Reactions.” Organic Letters (2020).
MLA
Click to copy
Jethava, Krupal P., et al. “Accelerated Reactivity Mechanism and Interpretable Machine Learning Model of N-Sulfonylimines toward Fast Multicomponent Reactions.” Organic Letters, 2020.
BibTeX Click to copy
@article{krupal2020a,
title = {Accelerated Reactivity Mechanism and Interpretable Machine Learning Model of N-Sulfonylimines toward Fast Multicomponent Reactions.},
year = {2020},
journal = {Organic Letters},
author = {Jethava, Krupal P. and Fine, Jonathan A and Chen, Yingqi and Hossain, Ahad and Chopra, G.}
}
Abstract
We introduce chemical reactivity flowcharts to help chemists interpret reaction outcomes using statistically robust machine learning models trained on a small number of reactions. We developed fast N-sulfonylimine multicomponent reactions for understanding reactivity and to generate training data. Accelerated reactivity mechanisms were investigated using density functional theory. Intuitive chemical features learned by the model accurately predicted heterogeneous reactivity of N-sulfonylimine with different carboxylic acids. Validation of the predictions shows that reaction outcome interpretation is useful for human chemists.
