Machine learning for NMR spectroscopy
Prediction of the spin system of small molecules from high-resolution liquid NMR spectra with the use of machine learning
At a glance
- Project leader : Dr. Andreas Henrici
- Deputy of project leader : Prof. Dr. Dirk Wilhelm
- Project team : Dr. Peter Bolt, Dr. Simon Bruderer, Dr. Flavio De Lorenzi, Dr. Michael Fey, Prof. Dr. Rudolf Marcel Füchslin, Dominik Graf, Dr. Björn Heitmann, Dr. Federico Paruzzo, Benjamin Ricchiuto, Nicolas Schmid, Dr. Giuseppe Toscano, Dr. Simone Ulzega, Dr. Thomas Oskar Weinmann, Dr. Volker Ziebart
- Project budget : CHF 571'499
- Project status : ongoing
- Funding partner : Innosuisse (Innovationsprojekt / Projekt Nr. 44786.1 IP-ENG)
- Project partner : Bruker Switzerland AG
- Contact person : Andreas Henrici
Description
The goal of this project is to make NMR spectroscopy available to a wider range of applications and to non-experts by the automation of data reduction and analysis steps, in particular by combining deep learning methods for the extraction and a Bayesian approach for the integration and refinement of information.
Publications
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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.,
2023.
Deconvolution of 1D NMR spectra : a deep learning-based approach.
Journal of Magnetic Resonance.
347(107357).
Available from: https://doi.org/10.1016/j.jmr.2022.107357
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Fischetti, Giulia; Schmid, Nicolas; Bruderer, Simon; Caldarelli, Guido; Scarso, Alessandro; Henrici, Andreas; Wilhelm, Dirk,
2023.
Automatic classification of signal regions in 1H nuclear magnetic resonance spectra.
Frontiers in Artificial Intelligence.
5(1116416).
Available from: https://doi.org/10.3389/frai.2022.1116416
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Schmid, Nicolas; Bruderer, Simon; Fischetti, Giulia; Paruzzo, Federico; Toscano, Giuseppe; Graf, Dominik; Fey, Michael; Ziebart, Volker; Henrici, Andreas; Grabner, Helmut; Wegner, Jan Dirk; Sigel, Roland K.O.; Heitmann, Björn; Wilhelm, Dirk,
2023.
Deconvolution of NMR spectra : a deep learning-based approach [poster].
In:
Datalab Symposium, Winterthur, Schweiz, 11. Januar 2023.
ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
Available from: https://doi.org/10.21256/zhaw-27429
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Fischetti, Giulia; Schmid, Nicolas; Bruderer, Simon; Paruzzo, Federico; Toscano, Giuseppe; Graf, Dominik; Fey, Michael; Henrici, Andreas; Scarso, Alessandro; Caldarelli, Guido; Heitmann, Björn; Wilhelm, Dirk,
2022.
A deep ensemble learning method for automatic classification of multiplets in 1D NMR spectra [poster].
In:
Prisner, Thomas, ed.,
EUROMAR 2022 Abstractbook.
European Conference on Magnetic Resonance (EUROMAR), Utrecht, The Netherlands, 10-14 July 2022.
ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
pp. 236.
Available from: https://doi.org/10.21256/zhaw-27328
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Schmid, Nicolas; Bruderer, Simon; Fischetti, Giulia; Paruzzo, Federico; Toscano, Giuseppe; Graf, Dominik; Fey, Michael; Henrici, Andreas; Grabner, Helmut; Wegner, Jan Dirk; Sigel, Roland K. O.; Heitmann, Björn; Wilhelm, Dirk,
2022.
Deconvolution of NMR spectra : a deep learning-based approach [poster].
In:
Prisner, Thomas, ed.,
EUROMAR 2022 Abstractbook.
European Conference on Magnetic Resonance (EUROMAR), Utrecht, The Netherlands, 10-14 July 2022.
ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
pp. 242.
Available from: https://doi.org/10.21256/zhaw-27336