||Prediction of Octanol/Water Partition Coefficient (K sub ow) with Algorithmically Derived Variables.
Niemi, G. J. ;
Basak, S. C. ;
Veith, G. D. ;
Grunwald, G. ;
||Environmental Research Lab.-Duluth, MN. ;Minnesota Univ.-Duluth. Natural Resources Research Inst.
Thermodynamic equilibrium ;
Organic compounds ;
Regression analysis ;
Prediction equations ;
Molecular structure ;
Hydrogen bonds ;
Octanol/water partition coefficients
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||A statistical model was developed with algorithmically derived independent variables based on chemical structure for prediction of octanol/water partition coefficients Kow measured for more than 4,000 chemicals. The procedure first classified the chemical into 14 groups based on the number of hydrogen bonds, and then best-subsets, multiple-regression analysis was used to predict Kow within groups. In addition, a training set/test set approach was used to provide an independent evaluation of the sensitivity of the model to the number of chemicals and variables used within each group. In general, the explained variation was higher and the standard error of the estimates (SEE) lower in the training sets as compared with the test set groups, whereas analyses of the combined data sets were generally intermediate. Explained variation among the 14 groups, using the combined data sets, ranged from 63 to 90%, and SEE ranged from 0.37 to 0.78 in logarithmic units. (Copyright (c) 1992 SETAC.)
||Pub. in Environmental Toxicology and Chemistry, v11 p893-900 Apr 92. Presented at the Symposium on Structure-Activity and Structure-Property Relationships (SARs) in Environmental Chemistry and Toxicology, Pacifchem '89, Honolulu, HI., December 17-22, 1989. Prepared in cooperation with Minnesota Univ.-Duluth. Natural Resources Research Inst.
|NTIS Title Notes
||Reprint: Prediction of Octanol/Water Partition Coefficient (K sub ow) with Algorithmically Derived Variables.
||PC A02/MF A01