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

• Agback, Tatiana

  • Department of Molecular Sciences, Swedish University of Agricultural Sciences
    

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