Modelling of Normal Boiling Points of Hydroxyl Compounds by Radial Basis Networks

Abstract

Abstract:Radial basis networks (RBN) were applied to link molecular descriptor and boiling points of 168 hydroxyl compounds. The total database was randomly divided into a training set(134), a validation set(17) and a testing set(17). Each compound in the lowest energy conformation was numerically characterized with E-dragon software. Then 8 molecular descriptors were selected to develop the RBN model. Simulated with the final optimum RBN model [8-35 (64) -1], the root mean square errors (RMSE) for the training, the validation and the testing set were 5.55, 4.28, and 5.33, and the correlation coefficients R=0. 994(training), 0.994(validation), 0.993(testing). The final RBN model was compared with the multiple linear regression approach and showed more satisfactory results.