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Quantitative structure‐property relationships for predicting henry's law constant from molecular structure
Author(s) -
Dearden John C.,
Schüürmann Gerrit
Publication year - 2003
Publication title -
environmental toxicology and chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 171
eISSN - 1552-8618
pISSN - 0730-7268
DOI - 10.1897/01-605
Subject(s) - solvation , molecular descriptor , linear regression , dimensionless quantity , quantitative structure–activity relationship , chemistry , partition coefficient , statistical physics , thermodynamics , correlation coefficient , test set , computational chemistry , molecule , biological system , mathematics , statistics , physics , organic chemistry , biology , stereochemistry
Various models are available for the prediction of Henry's law constant ( H ) or the air‐water partition coefficient ( K aw ), its dimensionless counterpart. Incremental methods are based on structural features such as atom types, bond types, and local structural environments; other regression models employ physicochemical properties, structural descriptors such as connectivity indices, and descriptors reflecting the electronic structure. There are also methods to calculate H from the ratio of vapor pressure ( p v ) and water solubility ( S w ) that in turn can be estimated from molecular structure, and quantum chemical continuum‐solvation models to predict H via the solvation‐free energy (Δ G s ). This review is confined to methods that calculate H from molecular strúcture without experimental information and covers more than 40 methods published in the last 26 years. For a subset of eight incremental methods and four continuum‐solvation models, a comparative analysis of their prediction performance is made using a test set of 700 compounds that includes a significant number of more complex and drug‐like chemical structures. The results reveal substantial differences in the application range as well as in the prediction capability, a general decrease in prediction performance with decreasing H , and surprisingly large individual prediction errors, which are particularly striking for some quantum chemical schemes. The overall best‐performing method appears to be the bond contribution method as implemented in the HEN‐RYWIN software package, yielding a predictive squared correlation coefficient ( q 2 ) of 0.87 and a standard error of 1.03 log units for the test set.