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Direct deduction of chemical class from NMR spectra
Date
2023
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.
Supervisor
Description
Publisher
Elsevier
Citation
Journal of Magnetic Resonance 348, 107381
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Funding code
Funding Information
Xunta de Galicia Galician Innovation Agency