Quantitative Study of 13C Chemical Shifts of β-Naphthalenes Using 2D Image Approach and Density Functional Theory Computation

Abstract

<span style="font-size: 9pt; mso-bidi-font-family: 'Times New Roman';"><span style="font-family: Times New Roman;">A 2D image approach has been used to predict¹³C NMR chemical shifts of β-naphthalene derivatives. In multivariate image analysis-Quantitative structure property relationship (MIA-QSPR) study, descriptors correlating with dependent variable are pixels (binaries) of 2D chemical structures; Variant pixels in the structures (substitutes) account to explained variance in the property (chemical shifts). A case study is carried out in order to predict ¹³C NMR chemical shifts of 10 carbon positions of 24 mono substituted β-naphthalenes. The resulted descriptors were subjected to principal component analysis (PCA) and the most significant principal components (PCs) were extracted. Then, MIA-QSPR modeling was done by means of principal component regression (PCR) and principal component –artificial neural network (PC-ANN) methods. A correlation ranking procedure is proposed here to select the most relevant set of PCs as inputs for PCR and PC-ANN modeling methods. Here, the ¹³C chemical shifts of studied compounds were predicted using density functional theory (DFT) calculations, too. The widely applied method of gauge included atomic orbital (GIAO) B3LYP/6-311++ G have been used. The performance of the GIAO was also compared with PCR and PC-ANN models. Results showed the superiority of the PC-ANN over GIAO and PCR models. Finally, ¹³C NMR chemical shifts of studied compounds were calculated using ChemDraw program. </span></span>