Application of multivariate image analysis in QSPR study of C-13 chemical shifts of naphthalene derivatives A comparative study

作者：Garkani Nejad Zahra^*; Poshteh Shirani Marziyeh

来源：Talanta, 2010, 83(1): 225-232.

DOI：10.1016/j.talanta.2010.09.012

• Multivariate image analysis (MIA)
• Quantitative structure-property relationship (QSPF)
• Principal component-artificial neural
• network (PC-ANN)
• C-13 chemical shift
• Naphthalene derivatives

A new implemented QSPR method whose descriptors achieved from bidimensional Images was applied for predicting C-13 NMR chemical shifts of 25 mono substituted naphthalenes The resulted descriptors were subjected to principal component analysis (PCA) and the most significant principal components (PCs) were extracted MIA-QSPR (multivariate image analysis applied to quantitative structure-property relationship) modeling was done by means of principal component regression (PCR) and principal component-artificial neural network (PC-ANN) methods Eigen value ranking (EV) and correlation ranking (CR) were used here to select the most relevant set of PCs as inputs for PCR and PC-ANN modeling methods The results supported that the correlation ranking-principal component-artificial neural network (CR-PC-ANN) model could predict the 13C NMR chemical shifts of all 10 carbon atoms in mono substituted naphthalenes with R-2 >= 0 922 for training set R-2 >= 0 963 for validation set and R-2 >= 0 936 for the test se

• 出版日期2010-11-15