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Metabolites
Volume 14
Issue 5
10.3390/metabo14050290
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Open AccessArticle
by Tanvir Sajed Tanvir Sajed SciProfilesScilitPreprints.orgGoogle Scholar Zinat Sayeeda Zinat Sayeeda SciProfilesScilitPreprints.orgGoogle Scholar Brian L. Lee Brian L. Lee SciProfilesScilitPreprints.orgGoogle Scholar Mark Berjanskii Mark Berjanskii SciProfilesScilitPreprints.orgGoogle Scholar Fei Wang Fei Wang SciProfilesScilitPreprints.orgGoogle Scholar Vasuk Gautam Vasuk Gautam SciProfilesScilitPreprints.orgGoogle Scholar David S. Wishart David S. Wishart SciProfilesScilitPreprints.orgGoogle Scholar
1
Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
2
Department of Computing Science, University of Alberta Edmonton, AB T6G 2E8, Canada
3
Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB T6G 2B7, Canada
4
Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB T6G 2H7, Canada
*
Author to whom correspondence should be addressed.
Metabolites 2024, 14(5), 290; https://doi.org/10.3390/metabo14050290
Submission received: 16 April 2024/Revised: 11 May 2024/Accepted: 16 May 2024/Published: 19 May 2024
(This article belongs to the Special Issue Metabolomics and Machine Learning for Improved Diagnostics and as a Tool to Accelerate Drug Development)
Abstract
NMR is widely considered the gold standard for organic compound structure determination. As such, NMR is routinely used in organic compound identification, drug metabolite characterization, natural product discovery, and the deconvolution of metabolite mixtures in biofluids (metabolomics and exposomics). In many cases, compound identification by NMR is achieved by matching measured NMR spectra to experimentally collected NMR spectral reference libraries. Unfortunately, the number of available experimental NMR reference spectra, especially for metabolomics, medical diagnostics, or drug-related studies, is quite small. This experimental gap could be filled by predicting NMR chemical shifts for known compounds using computational methods such as machine learning (ML). Here, we describe how a deep learning algorithm that is trained on a high-quality, “solvent-aware” experimental dataset can be used to predict 1H chemical shifts more accurately than any other known method. The new program, called PROSPRE (PROton Shift PREdictor) can accurately (mean absolute error of <0.10 ppm) predict 1H chemical shifts in water (at neutral pH), chloroform, dimethyl sulfoxide, and methanol from a user-submitted chemical structure. PROSPRE (pronounced “prosper”) has also been used to predict 1H chemical shifts for >600,000 molecules in many popular metabolomic, drug, and natural product databases.
Keywords: NMR; chemical shift; machine learning; graph neural network; predictor
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MDPI and ACS Style
Sajed, T.; Sayeeda, Z.; Lee, B.L.; Berjanskii, M.; Wang, F.; Gautam, V.; Wishart, D.S.Accurate Prediction of 1H NMR Chemical Shifts of Small Molecules Using Machine Learning. Metabolites 2024, 14, 290.https://doi.org/10.3390/metabo14050290
AMA Style
Sajed T, Sayeeda Z, Lee BL, Berjanskii M, Wang F, Gautam V, Wishart DS.Accurate Prediction of 1H NMR Chemical Shifts of Small Molecules Using Machine Learning. Metabolites. 2024; 14(5):290.https://doi.org/10.3390/metabo14050290
Chicago/Turabian Style
Sajed, Tanvir, Zinat Sayeeda, Brian L. Lee, Mark Berjanskii, Fei Wang, Vasuk Gautam, and David S. Wishart.2024. "Accurate Prediction of 1H NMR Chemical Shifts of Small Molecules Using Machine Learning" Metabolites 14, no. 5: 290.https://doi.org/10.3390/metabo14050290
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
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MDPI and ACS Style
Sajed, T.; Sayeeda, Z.; Lee, B.L.; Berjanskii, M.; Wang, F.; Gautam, V.; Wishart, D.S.Accurate Prediction of 1H NMR Chemical Shifts of Small Molecules Using Machine Learning. Metabolites 2024, 14, 290.https://doi.org/10.3390/metabo14050290
AMA Style
Sajed T, Sayeeda Z, Lee BL, Berjanskii M, Wang F, Gautam V, Wishart DS.Accurate Prediction of 1H NMR Chemical Shifts of Small Molecules Using Machine Learning. Metabolites. 2024; 14(5):290.https://doi.org/10.3390/metabo14050290
Chicago/Turabian Style
Sajed, Tanvir, Zinat Sayeeda, Brian L. Lee, Mark Berjanskii, Fei Wang, Vasuk Gautam, and David S. Wishart.2024. "Accurate Prediction of 1H NMR Chemical Shifts of Small Molecules Using Machine Learning" Metabolites 14, no. 5: 290.https://doi.org/10.3390/metabo14050290
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
Metabolites,EISSN 2218-1989,Published by MDPI
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