Prediction of 13C NMR Chemical Shifts of Quinolone Derivatives Based on DFT Calculations
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At present, there exists subjectivity in selecting descriptor sets for the quantitative structure property relationship (QSPR) models. A complete set is perfect, in which there is no any element redundant or needed to be added. This paper reports the complete sets of descriptors used to develop QSPR models for 13C NMR chemical shifts (δC parameters) of carbon atoms in quinolone derivatives. These descriptors in the complete sets used are calculated by applying the PBE1PBE functional of density functional theory (DFT) and the 6-311G(2d,2p) basis set. The multiple linear regression (MLR) technique and the support vector machine (SVM) algorithm are, respectively, used to develop linear and nonlinear QSPR models for δC parameters. The four QSPR models have the root mean square (RMS) errors less than 2.0 ppm, which approximately equal one fourth of the errors from the previous model. Further, our models have more samples in the test sets and less descriptors in the models. These results suggest that our four models of δC parameters have better statistical qualities. The feasibility of applying complete sets of descriptors to develop QSPR models for 13C NMR chemical shifts is demonstrated.
Keywords13C NMR chemical shifts DFT complete set of descriptors genetic algorithm MLR SVM
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