Deconvolution of 1D NMR spectra: A deep learning-based approach

[Display omitted] •Realistic synthetic 1D NMR spectra for robust performance on experimental spectra.•An implicit regularization through automatic labeling of synthetic spectra.•A custom data pre-processing for high dynamic range regions and broad lines.•A deep neural network that handles broad line...

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Bibliographic Details
Published in:Journal of magnetic resonance (1997) 2023-02, Vol.347, p.107357-107357, Article 107357
Main Authors: Schmid, N., Bruderer, S., Paruzzo, F., Fischetti, G., Toscano, G., Graf, D., Fey, M., Henrici, A., Ziebart, V., Heitmann, B., Grabner, H., Wegner, J.D., Sigel, R.K.O., Wilhelm, D.
Format: Article
Language:English
Subjects:
Deconvolution
Deep learning
Machine learning
NMR Spectroscopy
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Summary:[Display omitted] •Realistic synthetic 1D NMR spectra for robust performance on experimental spectra.•An implicit regularization through automatic labeling of synthetic spectra.•A custom data pre-processing for high dynamic range regions and broad lines.•A deep neural network that handles broad lines, crowded regions, and HDR spectra.•A fully automated method that yields expert-level quality sparse deconvolution results. The analysis of nuclear magnetic resonance (NMR) spectra to detect peaks and characterize their parameters, often referred to as deconvolution, is a crucial step in the quantification, elucidation, and verification of the structure of molecular systems. However, deconvolution of 1D NMR spectra is a challenge for both experts and machines. We propose a robust, expert-level quality deep learning-based deconvolution algorithm for 1D experimental NMR spectra. The algorithm is based on a neural network trained on synthetic spectra. Our customized pre-processing and labeling of the synthetic spectra enable the estimation of critical peak parameters. Furthermore, the neural network model transfers well to the experimental spectra and demonstrates low fitting errors and sparse peak lists in challenging scenarios such as crowded, high dynamic range, shoulder peak regions as well as broad peaks. We demonstrate in challenging spectra that the proposed algorithm is superior to expert results.
ISSN:1090-7807
1096-0856
DOI:10.1016/j.jmr.2022.107357