Three-Dimensional Quantitative Structure−Property Relationship (3D-QSPR) Models for Prediction of Thermodynamic Properties of Polychlorinated Biphenyls (PCBs):  Enthalpies of Fusion and Their Application to Estimates of Enthalpies of Sublimation and Aqueous Solubilities

Autor: Puri, S., Chickos, J. S., Welsh, W. J.
Zdroj: Journal of Chemical Information and Computer Sciences (now called Journal of Chemical Information and Modeling); January 2003, Vol. 43 Issue: 1 p55-62, 8p
Abstrakt: Comparative Molecular Field Analysis (CoMFA) has been used to develop three-dimensional quantitative structure−property relationship (3D-QSPR) models for the fusion enthalpy at the melting point (ΔfusHm(Tfus)) of a representative set of polychlorinated biphenyls (PCBs). Various alignment schemes, such as inertial, as is, atom fit, and field fit, were used in this study to evaluate the predictive capabilities of the models. The CoMFA models have also been derived using partial atomic charges calculated from the electrostatic potential (ESP) and Gasteiger−Marsili (GM) methods. The combination of atom fit alignment and GM charges yielded the greatest self-consistency (r2 = 0.955) and internal predictive ability (rcv2 = 0.783). This CoMFA model was used to predict ΔfusHm(Tfus) of the entire set of 209 PCB congeners, including 193 PCB congeners for which experimental values are unavailable. The CoMFA-predicted values, combined with previous estimations of vaporization and sublimation enthalpies, were used to construct a thermodynamic cycle that validated the internal self-consistency of the predictions for these three thermodynamic properties. The CoMFA-predicted values of fusion enthalpy were also used to calculate aqueous solubilities of PCBs using Mobile Order and Disorder Theory. The agreement between calculated and experimental values of solubility at 298.15 K, characterized by a standard deviation of ± 0.41 log units, demonstrates the utility of CoMFA-predicted values of fusion enthalpies to calculate aqueous solubilities of PCBs.
Databáze: Supplemental Index