QSAR analysis of soil sorption coefficients for polar organic chemicals: Substituted anilines and phenols

Water Research

Volumn 39, Issue 10, 2005, Pages 2048-2055

  Liu, Gousheng ^a   Yu, Jianguo ^b  

^a JIANGXI SCIENCE AND TECHNOLOGY NORMAL UNIVERSITY   (China)

^b EAST CHINA UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Artificial neural networks (ANNs); Polar organic chemicals; QSAR model; Soil sorption coefficients; Substituted anilines and phenols

Indexed keywords

MATHEMATICAL MODELS; MOLECULAR DYNAMICS; NEURAL NETWORKS; PHENOLS; QUANTUM THEORY; SOILS;

ANILINE; MOLECULAR CONNECTIVITY; POLAR ORGANIC CHEMICALS; SOIL SORPTION COEFFICIENTS;

SORPTION;

ANILINE; OCTANOL; ORGANIC COMPOUND; PHENOL; WATER;

ARTIFICIAL NEURAL NETWORK; SOIL CHEMISTRY; SORPTION;

ARTICLE; ENVIRONMENTAL PARAMETERS; MOLECULAR MECHANICS; PARTITION COEFFICIENT; PRIORITY JOURNAL; QUANTITATIVE STRUCTURE ACTIVITY RELATION; QUANTUM CHEMISTRY; REGRESSION ANALYSIS; STATISTICAL MODEL;

EID: 20344407072     PISSN: 00431354     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.watres.2005.03.030     Document Type: Article

References (29)

1. A.S. Abdul, T.L. Gibon, and D.N. Rai Statistical correlations for predicting partition coefficient for nonpolar organic contaminants between aquifer organic carbon and water Hazardous Waste Hazardous Mater. 4 1987 211 222
2. K. Anna, H. Mikael, T. Mats, and B. Bert Multivariate characterization of polycyclic aromatic hydrocarbons using semi-empirical molecule orbital calculations and physical data Chemosphere 50 5 2003 627 637
3. J.R. Baker, J.R. Mihelcic, and A. Sabljic Reliable QSAR for estimating Koc for persistent organic pollutants: correlation with molecular connectivity indices Chemosphere 45 2 2001 213 221
4. H.R. Bakul, and R.A. Shyam QSAR models to predict effect of ionic strength on sorption of chlorinated benzenes and phenols at sediment-water interface Water Res. 35 14 2001 3391 3401
5. A. Breindl, N. Beck, T. Clark, and R.C. Glen Prediction of the n-octanol/water partition coefficient, logP, using a combination of semiempirical MO-calculations and a neural network J. Mol. Model. 3 1997 142 155
6. J. Chen, W.J.G.M. Peijnenburg, W. Quan, Y. Zhao, D. Xue, and F. Yang The application of quantum chemical and statistical technique in developing QSPRs for the photohydrolysis quantum yields of substituted aromatic halides Chemosphere 37 1998 1169 1186
7. M.J.S. Dewar, E.G. Zoebisch, and E.F. Healy AM1: a new general purpose quantum mechanical molecular orbital method J. Am. Chem. Soc. 107 1985 3902 3908
8. D.W. Dietrich, N.D. Dreyer, and C.J. Hansch Confidence interval estimators for parameters associated with quantitative structure-activity relationships J. Med. Chem. 23 1980 1201 1205
9. A.L.R. Fabiana, and M.C.F. Marcia QSPR models of boiling point, octanol-water partition coefficient and retention time index of polycyclic aromatic hydrocarbons J. Mol. Struct.: THEOCHEM 663 1-3 2003 109 126
10. G.R. Famini, and L.Y. Wilson Using theoretical descriptors in QSAR: application to partition properties of alkyl (1-phenylsulfonyl) cycloalkane-carboxylates Chemosphere 35 1997 2417 2447
11. J. Hodson, and N.A. Williams The estimation of the adsorption coefficients (Koc) for soils, by high performance liquid chromatography Chemosphere 17 1988 66 77
12. J.J. Huuskonen, D.J. Livingstone, and I.V. Tetko Neural network modeling for estimation of partition coefficient based on atom-type electrotopological state indices J. Chem. Inf. Sci. 40 2000 947 955
13. R. Kaliszan Quantitative structure retention relationships Anal. Chem. 64 1992 619A 630A
14. L.B. Kier, and L.H. Hall Molecular Connectivity in Structure Activity Analysis [M] 1986 Research Studies Press Chichester
15. T. Kortvelyesi, M. Gorgenyi, and K. Heberger Correlation between retention indices and quantum chemical descriptors of ketones and aldehydes on stationary phases of different polarity Anal. Chim. Acta 428 2001 1773 1782
16. H.E. McClelland, and P.C. Jurs Quantitative structure-property relationships for the prediction of vapor pressures of organic compounds from molecular structures J. Chem. Inf. Sci. 40 2000 967 975
17. W. Meylan, P.H. Howard, and R.S. Boething Molecular topology/fragment contribution method for predicting soil sorption coefficients Environ. Sci. Technol. 26 1992 1560 1567
18. G.P. Moss, J.C. Dearden, H. Patel, and M.T.D. Cronin Quantitative structure-permeability relationships (QSPRs) for percutaneous absorption Toxicol. in Vitro 16 3 2002 299 317
19. M. Muller, and W. Kordel Comparison of screening methods for the estimation of adsorption coefficients on soil Chemosphere 32 12 1996 2493 2504
20. B.V. Oepen, W. Kordel, and W. Klein Sorption of polar and nonpolar compounds to soils. Processes, measurements and experience with the applicability of the modified OECD-guideline. 106 Chemosphere 22 1991 285 304
21. J. Pople, and D. Beveridge Molecule Orbitals Theory 1976 Science Press Beijing
22. F. Qu, Y. Yang, X. Feng, and Y. Dai An application of artificial neural networks and molecular topological index for the QSBR of phenolic organics Environ. Sci. 20 1999 16 19
23. Rumelhart, D., McClelland, J., 1986. In: Paralleled Distributed Processing, vol. 2. Bradford Books/MIT Press, Cambridge, MA.
24. A. Sabljic Prediction of the nature and strength of soil sorption of organic pollutants by molecular topology J. Agric. Food. Chem. 32 1984 243 246
25. A. Sabljic On the prediction of soil sorption coefficients of organic pollutants from molecular structures: application of molecular connectivity model Environ. Sci. Technol. 21 1987 358 366
26. A. Sabljic, and M. Protic Relationship between molecular connectivity indices and soil sorption coefficients of polycyclic aromatic hydrocarbons Bull. Environ. Contam. Toxicol. 28 1982 162 165
27. ow correlations Chemosphere 31 1995 4489 4514
28. K.J. Schaper, and M.L.R. Samitier Calculation of octanol/water partition coefficients (logP) using artificial neural networks and connection matrixes Quant. Struct. - Act. Relat. 16 1997 224 230
29. Werbos, P.J., 1974. Beyond regression: new tools for prediction and analysis in the behavioral science. Ph.D. Thesis, Harvard University, Cambridge, MA.