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

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 (R 5 0.933, RMS 5 12.61 uC). 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 5 4) yielded an R value of 0.822. A counterpropagation neural network was trained with the variables selected by the first tree, and reasonable results were achieved (R 5 0.748). In a test set of 9 new pyridinium bromides, all the low melting point cases were successfully identified.