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Empirical and DFT GIAO quantum-mechanical methods of 13C chemical shifts prediction: competitors or collaborators?
被引:31
|作者:
Elyashberg, Mikhail
[2
]
Blinov, Kirill
[2
]
Smurnyy, Yegor
[2
]
Churanova, Tatiana
[2
]
Williams, Antony
[1
]
机构:
[1] US Off, Royal Soc Chem, Wake Forest, NC 27587 USA
[2] Moscow Dept, Moscow 117513, Russia
关键词:
NMR;
C-13;
chemical shift prediction;
GIAO;
DFT;
HOSE code;
neural nets;
MOLECULAR-STRUCTURE ELUCIDATION;
ASSISTED STRUCTURE ELUCIDATION;
COMPLEX ORGANIC-MOLECULES;
2D NMR DATA;
COMPUTER-PROGRAM;
STEREOSTRUCTURE ASSIGNMENT;
STRUCTURAL ELUCIDATION;
STRUCTURE REVISION;
NATURAL-PRODUCTS;
NEURAL-NETWORK;
D O I:
10.1002/mrc.2571
中图分类号:
O6 [化学];
学科分类号:
0703 ;
摘要:
The accuracy of C-13 chemical shift prediction by both DFT GIAO quantum-mechanical (OM) and empirical methods was compared using 205 structures for which experimental and QM-calculated chemical shifts were published in the literature. For these structures, C-13 chemical shifts were calculated using HOSE code and neural network INN) algorithms developed within our laboratory. In total, 2531 chemical shifts were analyzed and statistically processed. It has been shown that, in general, QM methods are. capable of providing similar but inferior accuracy to the empirical approaches, but quite frequently they give larger mean average error values. For the structural set examined in this work, the following mean absolute errors (MAEs) were found: MAE(HOSE) = 1.58 ppm, MAE(NN) = 1.91 ppm and MAE(QM) = 3.29 ppm. A strategy of combined application of both the empirical and DFT GIAO approaches is suggested. The strategy could provide a synergistic effect if the advantages intrinsic to each method are exploited. Copyright (c) 2010 John Wiley & Sons, Ltd.
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页码:219 / 229
页数:11
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