Linear QSPRs for predicting pure compound properties in homologous series

Abstract

Linear QSPRs, containing 1 through 4 descriptors, are developed for predicting the normal boiling temperature, melting point temperature, and critical properties for the n‐alkane, 1‐alkene, n‐alkylbenzene, 1‐alcohol, and alkanoic monocarboxylic acid homologous series. It has been shown that property values for which experimental data are available can be predicted within experimental error level (with very few and very small exceptions), irrespective of whether interpolation or extrapolation is involved. Property values for which predicted literature data are available can be matched within the reported “reliability” level, even when extrapolation is carried out from very small training sets containing experimental data. Thus, the linear QSPRs developed represent well the nonlinear variation of the particular property with the carbon number, and increase the confidence in the values predicted when extrapolation is involved. © 2008 American Institute of Chemical Engineers AIChE J, 54: 978–990, 2008

Prediction of normal melting point of pure substances by a reference series method

“…Brauner et al 12 suggested the use of linear QSPR models for predicting T m of members of homologous series. This method was demonstrated by developing a linear QSPR containing four molecular descriptors (five parameters) for the 1-alkanol homologous series.…”

Adjustable QSPRs for prediction of properties of long‐chain substances

“…The T C data in Figure 6 exhibits some curvature which cannot be explained by a linear equation based on one descriptor. Indeed Brauner et al10 have shown that often more than one descriptor (typically two for T C ) are needed to represent the behavior of a property with high precision and random residual, for the range of n C values used here. We assume, however, that the additional curvature is caused by the influence of the specific functional group (COOH in this particular case) and its effect diminishes for long‐chain molecules.…”

“…Current methods used to predict physical and thermodynamic properties can be classified into group contribution methods (GC2), asymptotic behavior correlations (ABCs3–6) and various quantitative structure property relationships (QSPRs7–10). All of these methods use available experimental data for low carbon number ( n C ) compounds to obtain either “group contribution” values or regression model parameter values.…”

Average Molecule Construction of Petroleum Fractions Based on 1H‐NMR

“…The method began with the pioneering study of pesticides by Hansch et al In 1962, Mills et al first used QSPR to predict the melting and boiling points of organic compounds. Linear QSPRs were developed for predicting normal boiling temperature, melting temperature, and critical properties for several homologous series . With the development of QSARs/QSPRs, many studies have provided guidance on establishing correlation models …”