NMR signal processing, prediction, and structure verification with machine learning techniques

Carlos Cobas

doi:10.1002/mrc.4989

journal article Jan 27, 2020

NMR signal processing, prediction, and structure verification with machine learning techniques

Carlos Cobas

Magnetic Resonance in Chemistry Vol. 58 No. 6 pp. 512-519 · Wiley

View at Publisher Save 10.1002/mrc.4989

Abstract

Abstract
Machine learning (ML) methods have been present in the field of NMR since decades, but it has experienced a tremendous growth in the last few years, especially thanks to the emergence of deep learning (DL) techniques taking advantage of the increased amounts of data and available computer power. These algorithms are successfully employed for classification, regression, clustering, or dimensionality reduction tasks of large data sets and have been intensively applied in different areas of NMR including metabonomics, clinical diagnosis, or relaxometry. In this article, we concentrate on the various applications of ML/DL in the areas of NMR signal processing and analysis of small molecules, including automatic structure verification and prediction of NMR observables in solution.

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References

Details

Published: Jan 27, 2020
Vol/Issue: 58(6)
Pages: 512-519
License: View

Authors

C

Carlos Cobas

Mestrelab Research Santiago de Compostela Spain

Cite This Article

Carlos Cobas (2020). NMR signal processing, prediction, and structure verification with machine learning techniques. Magnetic Resonance in Chemistry, 58(6), 512-519. https://doi.org/10.1002/mrc.4989

NMR signal processing, prediction, and structure verification with machine learning techniques

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