Corrlation Of Quantitative Structure And Inhibition Phytotoxicity For Aromatic Compounds Using Ab Initio

In organic contamination chemistry and ecological toxicology, the quantitative structure-activity relationship (QSAR) technology may have two basic functions. First of all, it can be used for predicting or assessing the unknown environmental behavior and ecological toxicity of some chemicals. On the other hand, QSAR is very helpful in studying the mechanism of toxicity for a variety of pollutants and is of theoretical significance in pollution control and risk minimization. Therefore, optimum QSAR models may not only have good predictive power, but also possess explicit physical interpretations.Usually quantum chemical calculation is an important way to get structural parameters of specific molecules in study of the structure-activity relationship of organic pollutants. With quantum chemical calculation, all the information about electronic and spatial structures of specific compounds can be obtained, including molecular orbital energy level, atomic charge density, polarizability, and electrostatic charge of molecules. As widely considered, ab initio is theoretically precise, and thus it has become an effective scientific tool in quantum chemical calculation worldwide, due to the rapid development of computer science.The structural and thermodynamic parameters of 29 aromatic compounds were calculated at different basis sets of Hartree-Fock(HF) and density functional theory(DFT) in ab initio, in which gas phase system(GPS), self-consistency reaction field(SCRF) and natural bond orbital(NBO) were selected, respectively. Afterwards multiple stepwise regression was applied in fitting the quantitative structure-activity relationship(QSAR) between the properties of solute-solvent interactions and the specific parameters, using the programs of SPSS11.5 for windows and GQSARF2.0. These physiochemical properties were represented by the inhibition toxicity on germination rate of cucumber seeds.On the other hand, the colinearity between descriptors of the QSAR models were observed, and the leave-one-out(LOO) cross validation was also carried out for partial samples to validate the predictive power of the developed models. By comparison to the model with the same variables from semiempirical AM1 method, it is suggested...