PREDICTION OF BOILING POINTS OF ORGANIC HETEROCYCLIC-COMPOUNDS USING REGRESSION AND NEURAL-NETWORK TECHNIQUES

High quality models which relate structural descriptors to normal boil ing points have been developed for large, diverse groups of heterocycl ic compounds using both linear regression and neural network technique s. Parallel experiments were designed to compare the performance of th ese complementary modeling techniques on two different data sets. A fo rmerly studied data set comprised of 299 tetrahydrofuran (THF), thioph ene, furan, and pyran compounds was reexamined using neural networks. In addition, a new data set of 572 pyridine compounds was investigated to increase our understanding of the nitrogen-containing heterocycles . First, several new descriptors were developed to explore chemical pr inciples which govern the boiling point process. In particular, descri ptors that reflect hydrogen bonding and dipole-dipole interactions pro ved especially useful for improving the predictive models in the pyrid ine regression work. With each data set, neural networks were trained to predict boiling points with close to experimental accuracy using th e back-propagation learning algorithm. Results from these boiling poin t investigations show that once the key structural features are indent ified through traditional regression techniques, neural networks gener ally provide access to superior predictive equations. On the basis of this information, further studies were initiated to explore using neur al networks as a tool to upgrade, structural feature selection. Result s from this phase of the study demonstrate that this methodology can b e used to identify the most informationally rich descriptors.