Direct deduction of chemical class from NMR spectra

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Title: Direct deduction of chemical class from NMR spectra
Authors: Stefan Kuhn, Carlos Cobas, Agustin Barba, Simon Colreavy-Donnelly, Fabio Caraffini, Ricardo Moreira Borges
Date: Epub 2023 Jan, 2023 Mar
Reference: J Magn Reson. 2023 Mar:348:107381
DOI: 10.1016/j.jmr.2023.107381
Download link: https://pubmed.ncbi.nlm.nih.gov/36706464/

 

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.

 

 

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