Direct deduction of chemical class from NMR spectra
| 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.
Related posts
In vivo NMR is evolving into an important tool to understand biological processes and environmental responses. Current approaches use flow systems to sustain the organisms with oxygenated water and food (e.g., algae) inside the NMR.
Spectroscopic techniques, particularly NMR, frequently encounter the challenge of overlapping peaks. Thus, the need to enhance the resolution of such spectra has been the subject of extensive research for decades
Play FID is a new tool included within the latest version of Mnova 15.0.1. The purpose of Play FID is to provide an auditory representation of NMR data, enhancing the interpretation and understanding of NMR spectra.