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Direct deduction of chemical class from NMR spectra

Stefan Kuhn, Carlos Cobas, Agustin Barba, Simon Colreavy-Donnelly, Fabio Caraffini Orcid Logo, Ricardo Moreira Borges

Journal of Magnetic Resonance, Volume: 348, Start page: 107381

Swansea University Author: Fabio Caraffini Orcid Logo

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DOI (Published version): 10.1016/j.jmr.2023.107381

Abstract

This paper presents a proof-of-concept method for classifying chemical compounds directly from NMR data without performing structure elucidation. This can help to reduce the time in finding good structure candidates, as in most cases matching must be done by a human engineer, or at the very least a...

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Published in: Journal of Magnetic Resonance
ISSN: 1090-7807
Published: Elsevier BV 2023
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa62394
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Abstract: This paper presents a proof-of-concept method for classifying chemical compounds directly from NMR data without performing structure elucidation. This can help to reduce the time in finding good structure candidates, as in most cases matching must be done by a human engineer, or at the very least a process for matching must be meaningfully interpreted by one. The method identified as suitable for classification is a convolutional neural network (CNN). Other methods, including clustering and image registration, have not been found to be suitable for the task in a comparative analysis. The result shows that deep learning can offer solutions to spectral interpretation problems.
Keywords: NMR; Chemical classification; Image processing; Convolutional neural network; Deep learning
College: Faculty of Science and Engineering
Funders: S.K acknowledges funding by De Montfort University for computational facilities (VC2020 new staff L SL 2020). C.C and A.B thank Xunta de Galicia for funding the Mestrelab Research Center (CIM), subsidized by the Galician Innovation Agency, through the business aid program for the creation and integration of new business research centers 001_IN853D_2022.
Start Page: 107381

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