Direct deduction of chemical class from NMR spectra

Stefan Kuhn; Carlos Cobas; Agustin Barba; Simon Colreavy-Donnelly; Fabio Caraffini; Ricardo Moreira Borges

doi:10.1016/j.jmr.2023.107381

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arXiv · 2211.03173

Direct deduction of chemical class from NMR spectra

Stefan Kuhn·Carlos Cobas·Agustin Barba·Simon Colreavy-Donnelly·Fabio Caraffini·Ricardo Moreira Borges

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Abstract

This paper presents a proof-of-concept method for classifying chemical compounds directly from NMR data without doing structure elucidation. This can help to reduce 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. Therefore, for a long time automation in the area of NMR has been actively sought. The method identified as suitable for the classification is a convolutional neural network (CNN). Other methods, including clustering and image registration, have not been found suitable for the task in a comparative analysis. The result shows that deep learning can offer solutions to automation problems in cheminformatics.

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Stefan Kuhn, Carlos Cobas, Agustin Barba, Simon Colreavy-Donnelly, Fabio Caraffini, Ricardo Moreira Borges. 2022-11-06. Direct deduction of chemical class from NMR spectra. https://doi.org/10.1016/j.jmr.2023.107381

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