QSAR Model For Predicting Reaction Rate Constants Of Chlorine Atom With Organic Chemicals

New findings on the formation of chlorine radicals in continental urban areas make chlorine radicals (·Cl) play a more important role in governing the fate of tropospheric organic pollutants than what was previously thought. In the view of environmental risk assessment of organic chemicals, the reaction rate constant for chlorine atom (kCl) with organic chemicals is necessary. There are two methods for obtaining KCl data of organics:one is experimental measurement, the other is model prediction. Experimental measurement has obvious limitatation in determining the kCl data, such as the high cost in time, manpower and financial resources. Currently, only five hundred experimental kCl data for atmospheric organics are available, which is difficult to meet the need for ecological risk assessment. The quantitative structure-activity relationship (QSAR) models can overcome these problems. Based on the experimental data, QSAR model is developed in this study.According to the guidelines established by Organization for Economic Co-operation and Development (OECD) for QSAR, this study developed a QSAR model for predicting kCl values. 550 experiemental kCl values were collected from extensively enormous number of papers. Many kinds of quantum chemical descriptors, which has clear definitions, as well as DRAGON descriptors were selected and calculated based on optimized geometry at a higher theoretical level. The predictive model, which considers abundant species of organics and comprehensive reaction pathways, is established by multiple stepwise regression analysis. The adjusted determination coefficient (R2adjj) for the model is 0.825, indicating the goodness-of-fit. Q2LOO and Q2BOOT are 0.814,0.792 respectively, indicating the robustness of the model. The statistical papameters (R2adj.ext, Q2ext) are 0.814 and 0.721, indicating good predictive performances. All VIF values are under 10, indicating that the model is free of collinearity. Eculidean distance method is used to describe applicability domain(AD), which reveals that the model is applicable to the organics including the following functional groups:>C=C<,-C=C-,>C=O,-CHO,-COOH,-O-,-OH,-C=O(O)-,-X(Cl, F, Br),-NH2,-NH-,-N-N-,-S-,-C6H5,-NO2. And mechanism interpretation is done for the model. What's more, it is the first research that discussed the influence of functional groups on reactivity between chlorine atom and organic chemicals from the aspect of molecule structure.This study is planning to add the large dataset to the "Environmental Behavior Parameter Database", as well as import the established QSAR model into "Platform of Chemical Predictive Toxicology", which is an computer software platform established by our group. Thus, the research is applied and the rapid prediction of kCl values will be achieved by 'one click' conveniently.