Evaluation of some density functional methods for the estimation of hydrogen and carbon chemical shifts of phosphoramidates

Oliveira, Fabrício Marques de; Barbosa, Luiz C. A.; Fernandes, Sérgio Antônio; Lage, Mateus R.; Carneiro, José Walkimar de M.; Kabeshov, Mikhail A.

doi:10.1016/j.comptc.2016.06.025

Cited by 5 publications

(2 citation statements)

References 54 publications

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“…Since we do not have experimental NMR data for the 11 thousand molecules in our two sets, in step II we create representative NMR data for comparisons: the calculated NMR spectra (generated in Step I) are considered as surrogate experimental shifts data and new lists of chemical shifts are created synthetically by adding Gaussian distributed noise. Although the error distributions of NMR chemical shifts were reported to also obey a student t-distribution in other studies [131,[146][147][148][149], we assume errors for both carbons and protons follow a Gaussian distribution [144,150] with mean µ and standard deviation σ. Unless otherwise stated, the mean is assigned as 0, since the errors of scaled 13 C and 1 H NMR chemical shifts are equally likely to be positive or negative [144,147].…”

Section: Algorithmmentioning

confidence: 99%

An initial investigation of accuracy required for the identification of small molecules in complex samples using quantum chemical calculated NMR chemical shifts

et al. 2022

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The majority of primary and secondary metabolites in nature have yet to be identified, representing a major challenge for metabolomics studies that currently require reference libraries from analyses of authentic compounds. Using currently available analytical methods, complete chemical characterization of metabolomes is infeasible for both technical and economic reasons. For example, unambiguous identification of metabolites is limited by the availability of authentic chemical standards, which, for the majority of molecules, do not exist. Computationally predicted or calculated data are a viable solution to expand the currently limited metabolite reference libraries, if such methods are shown to be sufficiently accurate. For example, determining nuclear magnetic resonance (NMR) spectroscopy spectra in silico has shown promise in the identification and delineation of metabolite structures. Many researchers have been taking advantage of density functional theory (DFT), a computationally inexpensive yet reputable method for the prediction of carbon and proton NMR spectra of metabolites. However, such methods are expected to have some error in predicted 13C and 1H NMR spectra with respect to experimentally measured values. This leads us to the question–what accuracy is required in predicted 13C and 1H NMR chemical shifts for confident metabolite identification? Using the set of 11,716 small molecules found in the Human Metabolome Database (HMDB), we simulated both experimental and theoretical NMR chemical shift databases. We investigated the level of accuracy required for identification of metabolites in simulated pure and impure samples by matching predicted chemical shifts to experimental data. We found 90% or more of molecules in simulated pure samples can be successfully identified when errors of 1H and 13C chemical shifts in water are below 0.6 and 7.1 ppm, respectively, and below 0.5 and 4.6 ppm in chloroform solvation, respectively. In simulated complex mixtures, as the complexity of the mixture increased, greater accuracy of the calculated chemical shifts was required, as expected. However, if the number of molecules in the mixture is known, e.g., when NMR is combined with MS and sample complexity is low, the likelihood of confident molecular identification increased by 90%.

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Section: Algorithmmentioning

confidence: 99%

An initial investigation of accuracy required for the identification of small molecules in complex samples using quantum chemical calculated NMR chemical shifts

et al. 2022

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show abstract

“…Since we do not have experimental NMR data for the 11 thousand molecules in our two sets, in step II we create representative NMR data for comparisons: the calculated NMR spectra (generated in Step I) are considered as surrogate experimental shifts data and new lists of chemical shifts are created synthetically by adding Gaussian distributed noise. Although the error distributions of NMR chemical shifts were reported to also obey a student t-distribution in other studies[132,[147][148][149][150], we assume errors for both carbons and protons follow a Gaussian distribution…”

mentioning

confidence: 99%

An Initial Investigation of Accuracy Required for the Identification of Small Molecules in Complex Samples Using Quantum Chemical Calculated NMR Chemical Shifts

Yesiltepe¹,

Govind²,

Metz³

et al. 2020

Preprint

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The majority of primary and secondary metabolites in nature have yet to be identified, representing a major challenge for metabolomics studies that currently require reference libraries from analyses of authentic compounds. Using currently available analytical methods, complete chemical characterization of metabolomes is infeasible for both technical and economic reasons. For example, unambiguous identification of metabolites is limited by the availability of authentic chemical standards, which, for the majority of molecules, do not exist. Computationally predicted or calculated data are a viable solution to expand the currently limited metabolite reference libraries, if such methods are shown to be sufficiently accurate. For example, determining nuclear magnetic resonance (NMR) spectroscopy spectra in silico has shown promise in the identification and delineation of metabolite structures. Many researchers have been taking advantage of density functional theory (DFT), a computationally inexpensive yet reputable method for the prediction of carbon and proton NMR spectra of metabolites. However, such methods are expected to have some error in predicted 13C and 1H NMR spectra with respect to experimentally measured values. This leads us to the question–what accuracy is required in predicted 13C and 1H NMR chemical shifts for confident metabolite identification? Using the set of 11,716 small molecules found in the Human Metabolome Database (HMDB), we simulated both experimental and theoretical NMR chemical shift databases. We investigated the level of accuracy required for identification of metabolites in simulated pure and impure samples by matching predicted chemical shifts to experimental data. We found 90% or more of molecules in simulated pure samples can be successfully identified when errors of 1H and 13C chemical shifts in water are below 0.6 ppm and 7.1 ppm, respectively, and below 0.5 ppm and 4.6 ppm in chloroform solvation, respectively. In simulated complex mixtures, as the complexity of the mixture increased, greater accuracy of the calculated chemical shifts was required, as expected. However, if the number of molecules in the mixture is known, e.g., when NMR is combined with MS and sample complexity is low, the likelihood of confident molecular identification increased by 90%.

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Performance study of the electronic and optical parameters of thermally activated delayed fluorescence nanosized emitters (CCX-I and CCX-II) via DFT, SCC-DFTB and B97-3c approaches

Chopra

2020

J Nanostruct Chem

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Evaluation of some density functional methods for the estimation of hydrogen and carbon chemical shifts of phosphoramidates

Cited by 5 publications

References 54 publications

An initial investigation of accuracy required for the identification of small molecules in complex samples using quantum chemical calculated NMR chemical shifts

An initial investigation of accuracy required for the identification of small molecules in complex samples using quantum chemical calculated NMR chemical shifts

An Initial Investigation of Accuracy Required for the Identification of Small Molecules in Complex Samples Using Quantum Chemical Calculated NMR Chemical Shifts

Performance study of the electronic and optical parameters of thermally activated delayed fluorescence nanosized emitters (CCX-I and CCX-II) via DFT, SCC-DFTB and B97-3c approaches

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