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標題: DFT-based Quantitative Structure-Activity Relationships for Predicting Mixture Toxicity of Organic Pollutants
作者: 張瓊文
Chang, Chiung-Wen
關鍵字: 密度泛函理論;DFT;定量結構活性關係;有機磷農藥;苯類化合物;有機污染物;density functional theory;QSAR;quantitative structure-activity relationships;organophosphorus pesticides;benzene;organic pollutants
出版社: 土壤環境科學系所
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The environment is often exposed to chemical mixtures from multiple sources. The toxicity of various chemical mixtures is higher than single chemicals. However, the vast majority of toxicity studies deal with single chemicals, and therefore the prediction of mixture toxicity becomes a necessary and vital issue. In recent years, the development of quantum mechanical theory was combined with the progress of computational technology, which means quantitative calculation can be conducted from the atomic or molecular structure of a substance with little or even without empirical results. Besides, the parameter calculated was directly connected to organic activity, toxicity, chemical reaction, to construct the projection patterns, among which the QSAR is generally used to project mixture toxicity now.
In this study, the objective is the binary mixtures toxicity of 12 benzene and its derivatives in the environment and 9 organophosphorus pesticides used with high frequency domestically, from which the DFT of quantum mechanical theory developed in recent years is used as a basis to build up the QSAR of toxicity prediction. The differences between Semi-empirical (AM1) and DFT (B3LYP) are discussed as well, and the prediction pattern will further be applied to each field to access mixtures and reach the goal of fast prediction.
The results suggest that the results of prediction pattern are similar from either B3LYP or AM1 that are used to calculate benzene and its derivatives and mixtures. When using one parameter to predict toxicity, total surface area (TSA), apolar surface area (APSA), electron affinity (EA), and chemical potential (μ) are major factors. As multi-parameters are concerned, the increase of reaction energy (ΔEAB) and global soft (S) are required as parameters. Thus, AM1 has priority for choices in the future due to its fast calculation. When constructing prediction patterns of toxicity, surface area is an important parameter no matter benzene and its derivatives or organophosphorus pesticides are concerned with single parameter. And surface area could be influenced by chemical polarity depending on different subjects of prediction. Moreover, groups of fat and fragrance are factors to influence toxicity as well, so the number of ring and atom are used as parameters while the variety of toxic substances are complicated. When predicting mixture toxicity with multi-parameters, TSA, Etot, η, S, and μ are necessary parameters. And avoid aromatic compounds induce difference, so use sum of ring (R) and sum of atoms (NO, NN, NS, NP, NCl). Besides, ΔEAB is an important parameter in mixture.

其他識別: U0005-1507200914004800
Appears in Collections:土壤環境科學系

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