The simulation of 13C nuclear magnetic resonance spectra of dibenzofurans using multiple linear regression analysis and neural networks

Regression equations have been developed to predict the 13C NMR chemical shifts of the carbon atoms for a set of 20 dibenzofurans. Seventeen compounds were used as a training set and three compounds were used as an external prediction set. Using a generalized simulated annealing algorithm for descriptor selection, a linear regression model with eight descriptors was found with acceptable errors. Computational neural networks using a Quasi-Newton training algorithm were also used to predict the chemical shifts. The neural network models produced errors in the range of 0.66–1.18 ppm. The data were divided into subsets for regression analysis and neural networks because the data formed two distinct groups, and the results are compared to those obtained with the data of the complete set.