Study Of The QSAR For The Selected Organic Pollutants

Quantitative structure-activity/property relationship(QSAR/QSPR)of organicpollutants is of great importance to ecological risk assessments of organic compounds,pollution control and pollution prevention, etc.Quantum chemical calculation is an important way to get structural parameters ofspecific molecules in the QSAR/QSPR study. Quantum chemical parameters have explicitphysical chemistry interpretation, and they can be used in not only discussing effect modebetween toxicity and acceptor but also studying the molecular characters affecting physicalchemistry property of organic pollutants. Due to Density Functional Methods (DFT) inquantum chemistry calculation methods have very strict theory bases, they have become aneffective tool in quantum chemistry calculation worldwide. Molecular connectivity index isanother important structure parameters in QSAR/QSPR study. Because they can describemolecular structure in quantity, they have come into wide use in QSAR/QSPR study.Temperature-constrained cascade correlation network (TCCCN) was devised based onfast, strong and self-organizational architecture. The use of temperature constraints in cascadecorrelation network can solve the effects of overfitting. Mark devised an improved radialbasis function neural network (RBFNN) based on forward selection, which can optimize theRBF widths to control model complexity. Support Vector Machine (SVM) is a novel type ofmachine learning method; it has rigorous theory background and remarkable generalizationperformance. This dissertation introduced these methods to environmental chemistry to buildQSAR/QSPR model, predict the toxicity of organic pollutants and physical properties oforganic compounds.A brief description of QSAR/QSPR realization process and research status was given inChapter 1 of this dissertation.In Chapter 2, firstly we introduced the parameters and research methods used inQSAR/QSPR. Then we described the principle of improved RBFNN, TCCCN and SVM indetail. At last we gave a review of the application of these methods, respectly.In Chapter 3, TCCCN, back-propagation neural network (BP) and multiple linearregression (MLR) were applied to QSAR modeling based on a set of 35 nitrobenzenederivatives and their acute toxicities. These structure quantum-chemical descriptors wereobtained from density functional theory (DFT). Stepwise multiple regression analysis wasperformed and model was obtained. The conventional R was 0.925, and cross-validation Rwas 0.87.The principal component analysis is used for parameter selection. RMS for trainingset using TCCCN and BP were 0.067, 0.095 respectively, and RMS for testing set were 0.090,...