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Research article2020Peer reviewedOpen access

Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning

Qu, Xiaobo; Huang, Yihui; Lu, Hengfa; Qiu, Tianyu; Guo, Di; Agback, Tatiana; Orekhov, Vladislav; Chen, Zhong


Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental times. We present a proof-of-concept of the application of deep learning and neural networks for high-quality, reliable, and very fast NMR spectra reconstruction from limited experimental data. We show that the neural network training can be achieved using solely synthetic NMR signals, which lifts the prohibiting demand for a large volume of realistic training data usually required for a deep learning approach.


artificial intelligence; deep learning; fast sampling; NMR spectroscopy

Published in

Angewandte Chemie International Edition
2020, Volume: 59, number: 26, pages: 10297-10300

    UKÄ Subject classification

    Bioinformatics (Computational Biology)
    Analytical Chemistry

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