Prediction of chemical shift in NMR: a review

Date

2021-11-17

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

Wiley

Type

Article

Peer reviewed

Yes

Abstract

Calculation of solution-state NMR parameters, including chemical shift values and scalar coupling constants, is often a crucial step for unambiguous structure assignment. Data-driven (sometimes called \textit{empirical}) methods leverage databases of known parameter values to estimate parameters for unknown or novel molecules. This is in contrast to popular \textit{ab initio} techniques which use detailed quantum computational chemistry calculations to arrive at parameter estimates. Data-driven methods have the potential to be considerably faster than ab inito techniques and have been the subject of renewed interest over the past decade with the rise of high-quality databases of NMR parameters and novel machine learning methods. Here we review these methods, their strengths and pitfalls, and the databases they are built on.

Description

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.

Keywords

chemical shift prediction, NMR, machine learning, graph neural network

Citation

Jonas, E., Kuhn, S. and Schloerer, N.E. (2021) Prediction of chemical shift in NMR: a review. MRC,

Rights

Research Institute

Cyber Technology Institute (CTI)