Transforming NMR spectroscopy : extraction of multiplet parameters with deep learning

Schmid, Nicolas; Fischetti, Giulia; Henrici, Andreas; Wilhelm, Dirk; Wanner, Marc; Meshkian, Mohsen; Bruderer, Simon; Wegner, Jan-Dirk; Sigel, Roland K. O.; Heitmann, Bjoern; Konukoglu, Ender

doi:10.21256/zhaw-29510

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-29510

Publication type:	Conference poster
Type of review:	Peer review (abstract)
Title:	Transforming NMR spectroscopy : extraction of multiplet parameters with deep learning
Authors:	Schmid, Nicolas Fischetti, Giulia Henrici, Andreas Wilhelm, Dirk Wanner, Marc Meshkian, Mohsen Bruderer, Simon Wegner, Jan-Dirk Sigel, Roland K. O. Heitmann, Bjoern Konukoglu, Ender
et. al:	No
DOI:	10.21256/zhaw-29510
Proceedings:	Euromar 2022 Abstractbook
Editors of the parent work:	Prisner, Thomas
Page(s):	291
Conference details:	European Conference on Magnetic Resonance (EUROMAR), Utrecht, The Netherlands, 10-14 July 2022
Issue Date:	Jul-2023
Publisher / Ed. Institution:	ZHAW Zürcher Hochschule für Angewandte Wissenschaften
Language:	English
Subjects:	NMR spectroscopy; Machine learning; Deep learning
Subject (DDC):	006: Special computer methods 530: Physics
Abstract:	Accurate extraction of multiplet parameters, such as J-couplings and chemical shifts, play a vital role in small molecule analysis using nuclear magnetic resonance (NMR) spectroscopy. These parameters provide essential quantitative information about molecular structures, interatomic interactions, and chemical environments, enabling precise characterization of small organic compounds. This poster presents an innovative omputational approach that utilizes state-of-the-art deep learning techniques, specifically detection transformers, to automate and optimize the extraction of multiplet parameters from 1D NMR spectra of small molecules. By incorporating these advanced computational methods, experimenters can achieve improved efficiency, accuracy, and speed in analyzing and characterizing small organic compounds using NMR spectroscopy.
URI:	https://digitalcollection.zhaw.ch/handle/11475/29510
Fulltext version:	Published version
License (according to publishing contract):	Licence according to publishing contract
Departement:	School of Engineering
Organisational Unit:	Institute of Applied Mathematics and Physics (IAMP)
Published as part of the ZHAW project:	Maschinelles Lernen für NMR-Spektroskopie
Appears in collections:	Publikationen School of Engineering

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Schmid, N., Fischetti, G., Henrici, A., Wilhelm, D., Wanner, M., Meshkian, M., Bruderer, S., Wegner, J.-D., Sigel, R. K. O., Heitmann, B., & Konukoglu, E. (2023). Transforming NMR spectroscopy : extraction of multiplet parameters with deep learning [Conference poster]. In T. Prisner (Ed.), Euromar 2022 Abstractbook (p. 291). ZHAW Zürcher Hochschule für Angewandte Wissenschaften. https://doi.org/10.21256/zhaw-29510

Schmid, N. et al. (2023) ‘Transforming NMR spectroscopy : extraction of multiplet parameters with deep learning’, in T. Prisner (ed.) Euromar 2022 Abstractbook. ZHAW Zürcher Hochschule für Angewandte Wissenschaften, p. 291. Available at: https://doi.org/10.21256/zhaw-29510.

N. Schmid et al., “Transforming NMR spectroscopy : extraction of multiplet parameters with deep learning,” in Euromar 2022 Abstractbook, Jul. 2023, p. 291. doi: 10.21256/zhaw-29510.

SCHMID, Nicolas, Giulia FISCHETTI, Andreas HENRICI, Dirk WILHELM, Marc WANNER, Mohsen MESHKIAN, Simon BRUDERER, Jan-Dirk WEGNER, Roland K. O. SIGEL, Bjoern HEITMANN und Ender KONUKOGLU, 2023. Transforming NMR spectroscopy : extraction of multiplet parameters with deep learning. In: Thomas PRISNER (Hrsg.), Euromar 2022 Abstractbook. Conference poster. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. Juli 2023. S. 291

Schmid, Nicolas, Giulia Fischetti, Andreas Henrici, Dirk Wilhelm, Marc Wanner, Mohsen Meshkian, Simon Bruderer, et al. 2023. “Transforming NMR Spectroscopy : Extraction of Multiplet Parameters with Deep Learning.” Conference poster. In Euromar 2022 Abstractbook, edited by Thomas Prisner, 291. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. https://doi.org/10.21256/zhaw-29510.

Schmid, Nicolas, et al. “Transforming NMR Spectroscopy : Extraction of Multiplet Parameters with Deep Learning.” Euromar 2022 Abstractbook, edited by Thomas Prisner, ZHAW Zürcher Hochschule für Angewandte Wissenschaften, 2023, p. 291, https://doi.org/10.21256/zhaw-29510.