Quantitative structure–chromatographic retention relationship for polycyclic aromatic sulfur heterocycles

A quantitative structure-property relationship analysis of soot-water partition coefficients for persistent organic pollutants

Geometrical optimization and electrostatic potential calculations have been performed at the HF/6-31G level of theory for investigated persistent organic pollutants (POPs). A number of statistically based parameters have been obtained. Relationship between soot-water partition coefficients (logK(SC)) of POPs and the structural descriptors has been established by the multiple linear regression method. The result shows that the quantities derived from electrostatic potential V(s)(-)¯ and V(s,max), together with molecular surface area (A(S)) and the energy of the highest occupied molecular orbital (E(HOMO)) can be well used to express the quantitative relationship between structure and logK(SC) (QSPR) of POPs. Predictive capability of the model has been demonstrated by leave-one-out cross-validation with the cross-validated correlation coefficient of 0.9797. Furthermore, the predictive power of this model was further examined for the external test set with the correlation coefficient of 0.9811 between observed and predicted logK(SC), validating the robustness and good predictive ability of our model. Furthermore, in order to further investigate the applicability of these parameters derived from electrostatic potential in prediction of soot-water partition coefficient for organic pollutants, eleven polycyclic aromatic hydrocarbons (PAHs), eleven polychlorinated biphenyls (PCBs) and nine phenyl urea herbicides (PUHs) from other source have also been studied. The QSPR models established may provide a new powerful method for predicting soot-water partition coefficients (logK(SC)) of organic pollutants.

[Quantitative structure-gas chromatographic retention relationship of polycyclic aromatic sulfur heterocycles using molecular electronegativity-distance vector]

The chemical structures of 114 polycyclic aromatic sulfur heterocycles (PASHs) have been studied by molecular electronegativity-distance vector (MEDV). The linear relationships between gas chromatographic retention index and the MEDV have been established by a multiple linear regression (MLR) model. The results of variable selection by stepwise multiple regression (SMR) and the powerful predictive abilities of the optimization model appraised by leave-one-out cross-validation showed that the optimization model with the correlation coefficient (R) of 0.994 7 and the cross-validated correlation coefficient (Rcv) of 0.994 0 possessed the best statistical quality. Furthermore, when the 114 PASHs compounds were divided into calibration and test sets in the ratio of 2:1, the statistical analysis showed our models possesses almost equal statistical quality, the very similar regression coefficients and the good robustness. The quantitative structure-retention relationship (QSRR) model established may provide a convenient and powerful method for predicting the gas chromatographic retention of PASHs.

Extraction, separation and quantitative structure-retention relationship modeling of essential oils in three herbs

The essential oils extracted from three kinds of herbs were separated by a 5% phenylmethyl silicone (DB-5MS) bonded phase fused-silica capillary column and identified by MS. Seventy-four of the compounds identified were selected as origin data, and their chemical structure and gas chromatographic retention times (RT) were performed to build a quantitative structure-retention relationship model by genetic algorithm and multiple linear regressions analysis. The predictive ability of the model was verified by internal validation (leave-one-out, fivefold, cross-validation and Y-scrambling). As for external validation, the model was also applied to predict the gas chromatographic RT of the 14 volatile compounds not used for model development from essential oil of Radix angelicae sinensis. The applicability domain was checked by the leverage approach to verify prediction reliability. The results obtained using several validations indicated that the best quantitative structure-retention relationship model was robust and satisfactory, could provide a feasible and effective tool for predicting the gas chromatographic RT of volatile compounds and could be also applied to help in identifying the compound with the same gas chromatographic RT.

QSPR/QSAR models for prediction of the physico-chemical properties and biological activity of polychlorinated diphenyl ethers (PCDEs)

Polychlorinated diphenyl ethers (PCDEs) are a group of important persistent organic pollutants. In the present study, geometrical optimization and electrostatic potential calculations have been performed for all 209 PCDE congeners at the HF/6-31G(*) level of theory. A number of statistically-based parameters have been obtained. Linear relationships between gas-chromatographic relative retention time (RRT), n-octanol/water partition coefficient (log K(OW)), 298 K supercooled liquid vapour pressures (log p(L)), aqueous solubilities (logS(w,L)) and the immunotoxicity values (log ED(50)) of PCDEs and the structural descriptors have been established by multiple linear regression method. The result shows that the quantities derived from electrostatic potential V(s,min), SigmaV(s)(+),V(s,av)(-),Pi,sigma(tot)(2),sigma(+)(2),nu, and N(v)(+), together with the number of the chlorine atoms on the two phenyl rings (N(Cl)) can be well used to express the quantitative structure-property (activity) relationships of PCDEs. Good predictive capabilities have also been demonstrated by leave-group(1/5)-out cross-validation and external test set. Based on these equations, the predicted values have been presented for those PCDE congeners whose experimentally determined physico-chemical properties are unavailable.