Estimation of n-octanol/water partition coefficients of polycyclic aromatic hydrocarbons by quantum chemical descriptors

Quantitative structure-property relationship (QSPR) modeling is a powerful approach for predicting environmental behavior of organic pollutants with their structure descriptors. This study reports an optimal QSPR model for estimating logarithmic n-octanol/water partition coefficients (log K-OW) of polycyclic aromatic hydrocarbons (PAHs). Quantum chemical descriptors computed with density functional theory at B3LYP/6-31G(d) level and partial least squares (PLS) analysis with optimizing procedure were used for generating QSPR models for log K-OW of PAHs. The squared correlation coefficient (R-2) of the optimal model was 0.990, and the results of crossvalidation test (Q(cum)(2)=0.976) showed this optimal model had high fitting precision and good predictability. The log K-OW values predicted by the optimal model are very close to those observed. The PLS analysis indicated that PAHs with larger electronic spatial extent and lower total energy values tend to be more hydrophobic and lipophilic.