Estimation of melting points of pyridinium bromide ionic liquids with decision trees and neural networks

Abstract

Regression trees were built with an initial pool of 1085 molecular descriptors calculated by DRAGON software for 126 pyridinium bromides, to predict the melting point. A single tree was derived with 9 nodes distributed over 5 levels in less than 2 min showing very good correlation between the estimated and experimental values (R² = 0.933, RMS = 12.61 °C). A number n of new trees were grown sequentially, without the descriptors selected by previous trees, and combination of predictions from the n trees (ensemble of trees) resulted in higher accuracy. A 3-fold cross-validation with the optimum number of trees (n = 4) yielded an R² value of 0.822. A counterpropagation neural network was trained with the variables selected by the first tree, and reasonable results were achieved (R² = 0.748). In a test set of 9 new pyridinium bromides, all the low melting point cases were successfully identified.