NMR signal processing, prediction, and structure verification with machine learning techniques

scientific article published on 07 January 2020

NMR signal processing, prediction, and structure verification with machine learning techniques is …

instance of (P31):

scholarly article

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P356	DOI	10.1002/MRC.4989
P698	PubMed publication ID	31912547

P50	author	Carlos Cobas	Q88543614
P2860	cites work	Fast determination of 13C NMR chemical shifts using artificial neural networks	Q30949423
		Building blocks for automated elucidation of metabolites: machine learning methods for NMR prediction	Q33371981
		Structure-based predictions of 1H NMR chemical shifts using feed-forward neural networks	Q51997311
		Development of a fast and accurate method of 13C NMR chemical shift prediction	Q56402163
		The application of artificial neural networks in metabolomics: a historical perspective	Q90813747
P4510	describes a project that uses	machine learning	Q2539
P921	main subject	machine learning	Q2539
		signal processing	Q208163
P577	publication date	2020-01-07
P13046	publication type of scholarly work	review article	Q7318358
P1433	published in	Magnetic Resonance in Chemistry	Q3277097
P1476	title	NMR signal processing, prediction, and structure verification with machine learning techniques

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cites work (P2860)

Q114872882	A deep generative model enables automated structure elucidation of novel psychoactive substances
Q109827056	A pilot study for fragment identification using 2D NMR and deep learning
Q123966401	Deciding which is the best 1H NMR predictor for organic compounds using statistical tools
Q115153143	NMR-Based Chromatography Readouts: Indispensable Tools to “Translate” Analytical Features into Molecular Structures
Q108990857	Summary of DFT calculations coupled with current statistical and/or artificial neural network (ANN) methods to assist experimental NMR data in identifying diastereomeric structures

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