Quantitative structure-property relationship of distribution coefficients of organic compounds

The n-octanol/buffer solution distribution coefficient (or n-octanol/water partition coefficient) is of critical importance for measuring lipophilicity of drug candidates. After 4885 molecular descriptor generation, 15 molecular descriptors were selected to develop quantitative structure-property relationship (QSPR) models for distribution coefficients at pH 7.4 (log D _7.4) of a large data set consisting of 1043 organic compounds, which was divided into a training set (600 compounds) and a test set (443 compounds). Support vector machine (SVM) based on genetic algorithm was used to develop a model for log D _7.4 that has coefficient of determination r ² of 0.919 for the training set and 0.893 for the test set. The results suggest that the SVM model is accurate in predicting log D _7.4.

Prediction of Depuration Rate Constants for Polychlorinated Biphenyl Congeners

The integral equation formalism polarizable continuum model (IEF-PCM) for solvent effects with the default solvent (water) and solvent parameters, together with the density functional theory method at 6-31G(d) level, was used to optimize molecular structures for polychlorinated biphenyl (PCB) congeners. Four molecular descriptors were selected to develop quantitative structure-activity relationship (QSAR) models for the depuration rate constants (k _d) of 63 PCB congeners in a juvenile rainbow trout (Oncorhynchus mykiss). The optimal multiple linear regression (MLR) model has the correlation coefficient R of 0.933 and the root mean square (rms) error of 0.0681 for the total set of 63 PCB congeners. The support vector regression model has R of 0.953 and rms error of 0.0576 for the total set. Both the MLR and SVM QSAR models in this paper were accurate and acceptable compared with other QSAR models for the depuration rate of PCB congeners reported in references. Thus, applying IEF-PCM and B3LYP/6-31G(d) calculations for molecular descriptor derivation of PCB congeners is successful.