Prediction of the Normal Boiling Points of Organic Compounds from Molecular Structures with a Computational Neural Network Model

View Author InformationCite this: J. Chem. Inf. Comput. Sci. 1999, 39, 6, 974–983Publication Date:November 3, 1999https://doi.org/10.1021/ci990071lCopyright © 1999 American Chemical SocietyRIGHTS & PERMISSIONS

Abstract

Computational methods were used to link the molecular structures of diverse, industrially important, organic compounds from three different data sets to their normal boiling points. The data were provided by the Design Institute for Physical Property Data (DIPPR) Project 801 database. These data sets were composed of 298 hydrocarbons and heteroatom-containing structures including N compounds (data set I), 277 heteroatom-containing compounds excluding N compounds (data set II), and 104 halogen- and heteroatom-containing compounds, all of which contained at least 1 type of N-functional group (data set III). Each compound was represented by a set of calculated molecular structure descriptors. Genetic algorithms were used to select the best subsets of descriptors. Multiple linear regression and computational neural networks were employed to create the models best suited for the prediction of normal boiling points. This study used a nonlinear genetic algorithm program, for the first time on these data sets, to obtain the final models.

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