Modeling of 13C NMR chemical shifts of benzene derivatives using the RC–PC–ANN method: A comparative study of original molecular descriptors and multivariate image analysis descriptors
Zahra Garkani‐Nejad, Marziyeh Poshteh-Shirani · Canadian Journal of Chemistry · 2011
The primary goal of a quantitative structure–property relationship study is to identify a set of structurally based numerical descriptors that can be mathematically linked to a property of interest. In this work, two main groups of descriptors have been used to predict 13C NMR chemical shifts of ipso, ortho, meta, and para positions in a series of 113 monosubstituted benzenes. First, two groups of descriptors — original molecular descriptors (constitutional, topological, electronic, and geometrical) and multivariate image analysis (MIA) descriptors — were calculated. Then, calculated descriptors were subjected to principal component analysis and the most significant principal components were extracted. Finally, more correlated principal components were used as inputs of artificial neural networks. The results obtained using the rank correlation–principal component–artificial neural network (RC–PC–ANN) modeling method show high ability to predict 13C NMR chemical shifts. Also, comparison of the results indicates that MIA descriptors show better ability to predict 13C NMR chemical shifts than the original molecular descriptors.