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Research article - Peer-reviewed, 2020

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

Authors' information

Qu, Xiaobo
Xiamen Univ
Huang, Yihui
Xiamen Univ
Lu, Hengfa
Xiamen Univ
Qiu, Tianyu
Xiamen Univ
Guo, Di
Xiamen Univ Technol
Swedish University of Agricultural Sciences, Department of Molecular Sciences
Orekhov, Vladislav
Univ Gothenburg
Chen, Zhong
Xiamen Univ

UKÄ Subject classification

Bioinformatics (Computational Biology)
Analytical Chemistry

Publication Identifiers


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