USE OF A NEURAL-NETWORK TO DETERMINE THE NORMAL BOILING POINTS OF ACYCLIC ETHERS, PEROXIDES, ACETALS AND THEIR SULFUR ANALOGS

Models of relationships between structure and boiling point (bp) of 185 acyclic ethers, peroxides, acetals and their sulfur analogues have been constructed by means of a multilayer neural network (NN) using the back-propagation algorithm. The ability of a neural network to predict the boiling point of acyclic molecules containing polar atoms is outlined. The usefulness of the so-called embedding frequencies for the characterization of chemical structures in quantitative structure-property studies has been shown. NNs proved to give better results than multiple linear regression and other models in the literature.