SIMULATION OF C-13 NUCLEAR-MAGNETIC-RESONANCE SPECTRA OF TETRAHYDROPYRANS USING REGRESSION-ANALYSIS AND NEURAL NETWORKS

The C-13 NMR spectra of tetrahydropyrans are simulated directly from their molecular structures. A set of 29 tetrahydropyrans is used as a training set to generate regression equations and to train neural networks, and three additional compounds are used as a cross-validation set. The results of simulations done by regression analysis are found to be extremely sensitive to molecular geometries. To account for this, two different methods of descriptor manipulation, an averaging method and a Boltzmann-weighted averaging method, are introduced, and the models generated from the descriptor sets are compared. The results for the BolItzmann-weighted averaging method are found to be better than those based on descriptors derived from only the lowest energy conformation.