Analysis of water solubility data on the basis of HYBOT descriptors: Part 1. Partitioning of volatile chemicals in the water‐gas phase system

This work describes the analysis of water‐gas phase partitioning data L w =C w /C g for 559 organic chemicals on the basis of physicochemical descriptors calculated by the HYBOT program package. Physicochemical descriptors combined with indicator variables as well as a new approach combining traditi...

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Veröffentlicht in:QSAR & combinatorial science 2004-01, Vol.22 (9-10), p.926-942
Hauptverfasser: Raevsky, Oleg A., Schaper, Klaus‐J.
Format: Artikel
Sprache:eng
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Zusammenfassung:This work describes the analysis of water‐gas phase partitioning data L w =C w /C g for 559 organic chemicals on the basis of physicochemical descriptors calculated by the HYBOT program package. Physicochemical descriptors combined with indicator variables as well as a new approach combining traditional QSAR and molecular similarity are used to take structural features into account. The H‐bond acceptor ability of chemicals (i.e. interaction of acceptor atoms with hydrogen atoms of water) is the main factor that influences the partitioning of vapors into water. The simultaneous consideration of H‐bond acceptor and donor factors leads to a description of the solubility of vapors with a correlation coefficient of about 0.92. The influence of steric interactions of solutes (characterized by means of molecular polarizability) with water molecules contributes slightly but significantly from the statistics point of view. The use of a set of indicator variables for hydrocarbons and for molecules containing amino, amido, CX 3 , ether and nitro groups as well as for molecules with ability to form intramolecular hydrogen bonds improves the correlation and helps to take structural features into account. Furthermore, the application of an approach based on the calculation of additional contributions to solubility by considering ‘nearest neighbor chemicals’ and their difference in physicochemical parameters gives in many cases good results and could be very useful in the analysis of vast data sets.
ISSN:1611-020X
1611-0218
DOI:10.1002/qsar.200330843