Agback, Tatiana
- Department of Molecular Sciences, Swedish University of Agricultural Sciences
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
Abstract
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.
Keywords
artificial intelligence; deep learning; fast sampling; NMR spectroscopy
Published in
Angewandte Chemie International Edition
2020, volume: 59, number: 26, pages: 10297-10300
SLU Authors
UKÄ Subject classification
Bioinformatics (Computational Biology)
Analytical Chemistry
Publication identifier
- DOI: https://doi.org/10.1002/anie.201908162
Permanent link to this page (URI)
https://res.slu.se/id/publ/101752