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The Comparisons of Different Methods in QSAR and Their Applications in Environmental Science

Author: ChenDongXia
Tutor: YaoXiaoJun
School: Lanzhou University
Course: Chemoinformatics
Keywords: Quantitative structure - activity / property relationships Genetic algorithm - least squares support vector machine Local modeling Organic Pollutants
CLC: TP18
Type: Master's thesis
Year: 2011
Downloads: 100
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Abstract


In recent years, with the accelerated process of urbanization and rapid economic development, thousands of chemicals released into the environment, the environmental risk assessments of chemical substances has become increasingly important. The development of the related disciplines of computer science, statistics, physics, organic chemistry, biology, quantitative structure - activity / property relationship (Quantitative Structure-Activity/Property Relationship, QSAR / QSPR) method appear organics Environmental Risk Assessment an important and indispensable means. Accurate and efficient quantitative structure - activity / property relationship model, not only can quantitatively predict the migration of organic pollutants in the environment, conversion behavior and experimental study of the lag can be avoided, is the basis and premise of \therefore has important theoretical and practical significance. Persistent Organic Pollutants and the typical toxic organic pollutants, the papers for the study, to compare the different modeling methods in the study of different QSAR / QSPR This thesis is divided into the following chapters: The first chapter briefly organic pollutants environmental risk assessment, quantitative structure property relationship and its research progress. Chapter II of 64 persistent organic pollutants (POP) study, the genetic algorithm based on three different modeling methods - multiple linear regression, least squares support vector machine, local modeling used to predict a lasting organic pollutants in soil adsorption coefficient quantitative structure property relationship models. The local modeling method gives the best results, the training and test sets of the square of the correlation coefficient (R2) of 0.894 and 0.860, respectively, cross-validation coefficient (Q2) of 0.860. The model has good predictive ability and strong robustness, and can be used for the forecast of the organic pollutants in the soil adsorption coefficient. The third chapter, based on the density functional theory B3LYP method to obtain accurate three-dimensional molecular structure information to establish a quantitative structure property relationship models predict 70 PCBs organic carbon adsorption coefficient. Linear and nonlinear models based on multiple linear regression method and least squares support vector machine by genetic algorithm to select the appropriate descriptors. Different structural optimization method, relative to the semi-empirical quantum chemical method, has better prediction results based on the the QSPR model established by the B3LYP method of three-dimensional molecular structure. Chapter genetic algorithm (GA) and least squares support vector machine (LSSVM) combined for descriptor selection quantitative structure-property relationship study and model for the structure of the 96 organic pesticides and Henry constant between the quantitative relationship model. Compare the genetic algorithm descriptors - multiple linear regression (GA-MLR) method chosen, GA-LSSVM method gives better results. The genetic algorithm - least squares support vector machine method to establish the model, the training set R2 = 0.785, Q2 = 0.637, RMSE of = 1.010 for the prediction of the test set R2 0.734, RMSE of 1.171. Description genetic algorithm - least squares support vector machine (GA-LSSVM) is a potential descriptor selection and modeling method.

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