| The study of QSPR is to construct models between the structure of compounds and their physical, chemical properties and biological activity so as to develop new compounds and predict the properties of the compounds. It is a very active field in the application of computer in chemistry. In recent years, QSPR has been widely used in many fields of chemistry, biology, environment and technology, and combine with various experimental designs to the separation of many components. In this paper, QSPR was used to develop models to predict some signigicative properties of chemicals. QSPR and response surface experimental design were applied to capillary electrophoresis to optimize the separation conditions of active components in Chinese medicines. The results indicated that the combination of experimental design and QSPR was found to be a powerful tool in predicting separation conditions in CE.There are six parts in this dissertation, including a review on the background, theory, research method, modeling method and applications of quantitative structure property relationship (QSPR); the application of response surface experimental design and QSPR to the separation and determination of two active components of Chinese traditional medicine and preparations by capillary electrophoresis (CE); QSPR study on electrochemistry, optical properties, toxicity to environmental and biological activity of organic compounds by heuristic method (HM) and radial basis function neural network (RBFNN).In the first chapter, we reviews the background, research method, modeling method and the applications in analytical chemistry of QSPR .In chapter 2, a simple and rapid method for the separation and determination of honokiol and magnolol in Magnolia officinalis and its medicinal preparation is developed by response surface methodology and capillary zone electrophoresis. The condition was optimized and successfully applied to the analysis of honokiol and magnolol in Magnolia officinalis and Huoxiang Zhengqi Liquid. In addition, an artificial neural network with"3-7-1"structure based on the ratio of peak resolution to the migration time of the later component (Rs/t) given by Box-Behnken design is also reported and the predicted results are in good agreement with the values given by the mathematic software and the experimental results, RSD<5%. In chapter 3, Quantitative structure-property relationship (QSPR) models correlating half-wave potentials (E1/2) of dye intermediate 9, 10-anthraquinones and their structures were developed based on linear and non-linear modeling methods. Descriptors calculated from the molecular structures alone were used to represent the E1/2 of 9, 10-anthraquinones. Heuristic method (HM) was used to select the most appropriate molecular descriptors and a linear QSPR model was developed. Using the selected descriptors, radial basis function neural networks (RBFNN) was used in the non-linear model development. And the structural factors which have effect on E1/2 were discussed.In chapter 4, in an attempt to develop predictive tools for the determination of the UV maximum absorption wavelength (λmax), QSPR models forλmax of 50 coumarins were developed based on their structures alone. A six-descriptor linear correlation by heuristic method (HM) and a non-linear model using radial basis function neural network (RBFNN) approach were reported. Both of the models indicated satisfactory stability and predictive ability. The descriptors appearing in these models are discussed. This QSPR approach can contribute to a better understanding of structural factors of the organic compounds responsible for the analysis of maximum absorption wavelength.In chapter 5, the modeling methods used in chapter 4 was applied to develop quantitative structure-visible light absorption wavelength (λ) relationship model for photosensitizers in photodynamic therapy for cancer. A linear quantitative structure-visible light absorption wavelength (λ) relationship model of 142 photosensitizers was obtained. The results of cross-validation algorithm and external validation set indicated that the model has a satisfactory statistical stability and predictivity. It could be a potential way for instructing synthesis of this kind of new photosensitizers.In chapter 6, a quantitative structure-activity relationship (QSAR) was developed by the Heuristic method (HM) and Radial basis function neural networks (RBFNN) to study the tetrahymena pyriformis toxicity (explained as–logIGC50) of 48 aniline compounds. Cross-validation was used to evaluate the linear and non-linear models. The results show that both the two models have good stability and predictability for tetrahymena pyriformis toxicity of aniline derivatives.
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