ArticleJournal of magnetic resonance (San Diego, Calif. : 1997)2024
Neural net analysis of NMR spectra from strongly-coupled spin systems.
Article in Journal of magnetic resonance (San Diego, Calif. : 1997), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- The perspective of artificial intelligence for NMR.Magnetic resonance letters · 2027Review
- Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Extracting parameters such as chemical shifts and coupling constants from proton NMR spectra is often a first step in using spectra for compound identification and structure determination. This can become challenging when scalar couplings between protons are comparable in size to chemical shift differences (strongly coupled), as is often the case with low-field (bench top) spectrometers. Here we explore the potential utility of AI methods, in particular neural networks, for extracting parameters from low-field spectra. Rather than seeking large experimental sets of spectra for training a network, we chose quantum mechanical simulation of sets, something that is possible with modern software packages and computer resources. We show that application of a network trained on 2-D J-resolved spectra and applied to a spectrum of iduronic acid, shows some promise, but also meets with some obstacles. We suggest that these may be overcome with improved pulse sequences and more extensive simulations.
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Registered trials
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