Prediction of chemical shift in NMR: A review. (7th December 2021)

• Record Type: Journal Article
• Authors: Jonas, Eric; Kuhn, Stefan; Schlörer, Nils
• Other Names: Kuhn Stefan guestEditor.
• Is Part Of: Magnetic resonance in chemistry. Volume 60:Number 11(2022)
• Page Start: 1021
• Page End: 1031
• Publication Date: 2021-12-07
• Subjects: chemical shift prediction -- graph neural network -- machine learning -- NMR; Nuclear magnetic resonance spectroscopy -- Periodicals; Chemistry, Organic -- Periodicals; Magnetic resonance -- Periodicals; 538.36
• DOI: 10.1002/mrc.5234
• Languages: English
• ISSNs: 0749-1581

Abstract

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 empirical ) methods leverage databases of known parameter values to estimate parameters for unknown or novel molecules. This is in contrast to popular ab initio techniques that 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. Abstract : This review tracks the success of NMR shift prediction for 1 H and 13 C nuclei over time. It also gives an overview of the methods, including recent deep learning approaches. These have improved the results compared with earlier methods.