Prediction of substituent types and positions on skeleton of eudesmane-type sesquiterpenes using generalized regression neural network (GRNN)

Abstract

Sesquiterpenes are formed from countless biogenetic pathways and are therefore a constant challenge to spectroscopists in structure elucidation. In this study, we explore the ability of generalized regression neural network (GRNN), an architecture of artificial neural networks (ANNs), to predict the substituent types on eudesmanes, one of the most representative skeletons of sesquiterpenes. Carbon-13 (13C) nuclear magnetic resonance (NMR) chemical shift values of skeletons of 291 eudesmane sesquiterpenes were used as the input data used for the network. Each substituent type on the skeleton of the different compounds were coded and used as the output data for the network. These data were used to train the network. After training, the network was simulated using 34 test compounds. The results showed that the GRNN had between 73.33 to 100% recognition rates of the test compounds. GRNN could therefore be a powerful aid in the structural elucidation of organic compounds.   Key words: Artificial neural networks (ANNs), generalized regression neural network (GRNN), eudesmane skeleton, sesquiterpenes, structural elucidation.