title: QSPR for prediction of subcooled vapor pressures (log PL) of polychlorinated trans-azobenzenes.
abstract: In this study the values of subcooled vapor pressures (log P(L)) were estimated for 209 trans chloroazobenzenes (Ct-ABs) that fill some gaps in analytical and experimental data on these compounds. There are 209 chloro derivatives of trans azobenzenes that are relatively stable and more environmentally relevant than 209 chloro cis congeners. The calculations models were based on the Quantitative Structure-Property Relationship (QSPR) scheme using the semi-empirical method (PM6) in molecular package (MOPAC) software and density functional theory (DFT) method using B3LYP functional and 6-311++G** basis set) in Gaussian 03 software method and the artificial neural networks (ANNs) prediction. The values of log P(L) predicted by models used varied between -3.94 to -2.66 for Mono-; -4.85 to -2.97 for Di-; -5.18 to -3.17 for Tri-; -6.02 to -3.77 for Tetra-; -6.64 to -4.64 for Penta-; -7.36 to -4.76 for Hexa-; -7.54 to -5.79 for Hepta-; -7.75 to -6.64 for Octa-; -7.89 to -7.44 for Nona-Ct-Abs; and -8.09 and -8.13 for Deca-Ct-AB. Based on these values Ct-ABs can be grouped localized among relatively low (log P(L) -4 to -2) and low (log P(L) < -4) mobile Persistent Organic Pollutants (POPs). Both the calculation methods employed were characterized by similar prediction ability of subcooled vapor pressure values of Ct-ABs, while those of PM6 are much more efficient due to a cheaper hardware used and around 300-fold less time spent on calculations.
authors.affiliation: Institute of Environmental Sciences & Public Health, University of Gdańsk, Gdańsk, Poland.
authors.forename: Agata J
authors.initials: AJ
authors.lastname: Wilczyńska-Piliszek
authors.forename: Sławomir
authors.initials: S
authors.lastname: Piliszek
authors.forename: Jerzy
authors.initials: J
authors.lastname: Falandysz
mesh_terms: D001391:Azo Compounds / Q000737:chemistry* / Q000494:pharmacology*; D008962:Models, Theoretical; D015394:Molecular Structure; D016571:Neural Networks, Computer; D021281:Quantitative Structure-Activity Relationship; D012984:Software; D055548:Vapor Pressure
chemical_list: D001391:Azo Compounds; C009850:azobenzene