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Exploratory machine-learned theoretical chemical shifts can closely predict metabolic mixture signals

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Abstract

Various chemical shift predictive methodologies have been studied and developed, but there remains the problem of prediction accuracy. Assigning the NMR signals of metabolic mixtures requires high predictive performance owing to the complexity of the signals. Here we propose a new predictive tool that combines quantum chemistry and machine learning. A scaling factor as the objective variable to correct the errors of 2355 theoretical chemical shifts was optimized by exploring 91 machine learning algorithms and using the partial structure of 150 compounds as explanatory variables. The optimal predictive model gave RMSDs between experimental and predicted chemical shifts of 0.2177 ppm for δ1H and 3.3261 ppm for δ13C in the test data; thus, better accuracy was achieved compared with existing empirical and quantum chemical methods. The utility of the predictive model was demonstrated by applying it to assignments of experimental NMR signals of a complex metabolic mixture.

Graphical abstract: Exploratory machine-learned theoretical chemical shifts can closely predict metabolic mixture signals

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Publication details

The article was received on 15 Aug 2018, accepted on 23 Aug 2018 and first published on 10 Sep 2018


Article type: Edge Article
DOI: 10.1039/C8SC03628D
Citation: Chem. Sci., 2018, Advance Article
  • Open access: Creative Commons BY-NC license
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    Exploratory machine-learned theoretical chemical shifts can closely predict metabolic mixture signals

    K. Ito, Y. Obuchi, E. Chikayama, Y. Date and J. Kikuchi, Chem. Sci., 2018, Advance Article , DOI: 10.1039/C8SC03628D

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