Universität zu Köln

Jonas, Eric, Kuhn, Stefan and Schlorer, Nils . Prediction of chemical shift in NMR: A review. Magn. Reson. Chem.. HOBOKEN: WILEY. ISSN 1097-458X

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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.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Jonas, Eric UNSPECIFIED UNSPECIFIED UNSPECIFIED Kuhn, Stefan UNSPECIFIED UNSPECIFIED UNSPECIFIED Schlorer, Nils UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-587017
DOI:	10.1002/mrc.5234
Journal or Publication Title:	Magn. Reson. Chem.
Publisher:	WILEY
Place of Publication:	HOBOKEN
ISSN:	1097-458X
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language NUCLEAR-MAGNETIC-RESONANCE; COMPUTER-PROGRAM; ARTIFICIAL-INTELLIGENCE; STRUCTURE ELUCIDATION; NEURAL-NETWORKS; SPECTRA; SIMULATION; ASSIGNMENT; H-1; SUBSTRUCTURES Multiple languages Chemistry, Multidisciplinary; Chemistry, Physical; Spectroscopy Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/58701