Reaction Products Revised and Mechanisms Revisited with Machine Learning-Augmented Computational NMR
Srinivas Beduru, Enoch Asimbisa, Ivan M. Novitskiy, Andrei G. Kutateladze
TL;DR
This paper introduces a machine learning method to correct errors in organic reaction products and their mechanisms using NMR data.
Contribution
A machine learning-augmented DFT method is applied to revise incorrect reaction product structures and mechanisms.
Findings
DU8ML enables high-throughput revision of misassigned reaction products.
The method helps correct errors in reported reaction mechanisms.
It addresses an underexplored challenge in organic chemistry.
Abstract
DU8ML, a fast and accurate machine learning-augmented DFT method for computing NMR spectra, has proven effective for high-throughput revision of misassigned natural products. In this paper, we continue with another important underexplored challenge: revision of incorrect product structures of reported organic reactions, often necessitating correction of reaction mechanisms.
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Taxonomy
TopicsMachine Learning in Materials Science · Molecular spectroscopy and chirality · Computational Drug Discovery Methods