Simulation of the 13C nuclear magnetic resonance spectra of trisaccharides using multiple linear regression analysis and neural networks

Predictive models are developed for the 13C NMR chemical shifts of the carbon atoms comprising the central rings of 46 trisaccharide compounds. Thirty-nine trisaccharides are used as a training set for development of models using regression analysis and computational neural networks, and seven compounds are used as an external prediction set. The descriptors used in the models are developed directly from the molecular structures of the trisaccharides. Three different methods of descriptor selection are compared. The dependence of the models on the geometries of the trisaccharides is explored. The models developed with geometric descriptors are better than those developed without geometric descriptors, although the latter models are still of a comparable quality. Overall, the best model found is a neural network based on descriptors selected by multiple linear regression.