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Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning.

Journal article
Authors Xiaobo Qu
Yihui Huang
Hengfa Lu
Tianyu Qiu
Di Guo
Tatiana Agback
Vladislav Orekhov
Zhong Chen
Published in Angewandte Chemie
Volume 59
Issue 26
Pages 10297-10300
ISSN 1521-3773
Publication year 2020
Published at Swedish NMR Centre at Göteborg University
Department of Chemistry and Molecular Biology
Pages 10297-10300
Language en
Subject categories Structural Biology, Spectroscopy


Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental time. We present a proof-of-concept of application of deep learning and neural network 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 signal, which lifts the prohibiting demand for large volume of realistic training data usually required in the deep learning approach.

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