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

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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 (R-2 = 0.933, RMS = 12.61 degreesC). 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 R-2 value of 0.822. A counterpropagation neural network was trained with the variables selected by the first tree, and reasonable results were achieved (R-2 = 0.748). In a test set of 9 new pyridinium bromides, all the low melting point cases were successfully identified.
Original languageEnglish
Pages (from-to)20-27
Number of pages8
JournalGreen Chemistry
Volume7
Issue number1
DOIs
Publication statusPublished - 1 Jan 2005

Keywords

  • pyridinium derivative
  • unclassified drug
  • pyridinium bromide

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