Review and Prospect: Deep Learning in Nuclear Magnetic Resonance Spectroscopy.

Journal: Chemistry (Weinheim an der Bergstrasse, Germany)
Published Date: Jun 25, 2020

Abstract

Since the concept of deep learning (DL) was formally proposed in 2006, it has had a major impact on academic research and industry. Nowadays, DL provides an unprecedented way to analyze and process data with demonstrated great results in computer vision, medical imaging, natural language processing, and so forth. Herein, applications of DL in NMR spectroscopy are summarized, and a perspective for DL as an entirely new approach that is likely to transform NMR spectroscopy into a much more efficient and powerful technique in chemistry and life sciences is outlined.

Authors

  • Dicheng Chen
    Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, P.O. Box 979, Xiamen, 361005, P.R. China.
  • Zi Wang
    Clinical Medical College, Yangzhou University, 225009 Yangzhou, Jiangsu, China.
  • Di Guo
  • Vladislav Orekhov
    Department of Chemistry and Molecular Biology, University of Gothenburg, Box 465, Gothenburg, 40530, Sweden.
  • Xiaobo Qu

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