Atomic Equilibrium Electronegativity And Its Application Research In Molecular Design And Molecular Modeling

Quantitative structure-property/activity relationship (QSPR/QSAR) was originally introduced as a branch in the biological field and developed in response to rational design of bioactivity molecules. At present, QSPR/QSAR research had become a basis topic and important tache for molecular design and R&D of new goal compounds, and was also an important assessment method of environmental toxicity for chemicals. It had been widely used for the prediction of various physicochemical properties and biological activities of organic compounds by using different statistical methods and various kinds of molecular descriptors. In this thesis, based on the molecular design, atomic equilibrium electronegativity and molecular structrural descriptors were utilized to establish the QSPR/QSAR models in order to estimate and predict compound properties, biological activities and environmental toxicties. The main contents and conclusions were given as follows:1. In this paper, a brief review of principle, research methods and current status for QSPR/QSAR, molecular design and molecular modelling, atomic equilibrium electronegativity and atomic charge were presented. In this section, the research progress of applications in QSPR/QSAR, molecular design and molecular modelling, equilibrium electronegativity and atomic charge were introduced in detail.2. Based on the molecular graphic theory, novel molecular structure descriptors of electrical connectivity index0Kv,1Kv and the imidazoline ring of non-hydrogen atoms balance total charge fraction (MCI) was proposed for expression of local chemical microenvironment and atomic hybridation state. A quantitative structure-property relationship (QSPR) of estimating fifteen imidazoline corrosion inhibitors efficiency (CIE) for anti-corrosion behavior towards hydrogen sulfide and carbon dioxide was established including descriptors0Kv,1Kv and MCI. The results showed that correlation coefficient of modelling calculated and leave-one-out cross-validation (LOO-CV) predicted value were0.9764and0.9546, respectively. The QSPR model was of good stability and external predictive capability. For the same purpose, artificial neural network was applied and the result was improved. The results proposed that increasing substitution length of the imidazoline ring, reducing the molecular branching and lowering the imidazoline ring of non-hydrogen atoms balance total charge fraction had a significant effect.3. Two novel topological electro-negativity indices based on distance matrix, named YC and We indices, were put forward and could be used for modelling properties of multiple bond organic compounds by equilibrium electro-negativity of atom and relative bond length of molecular. A quantitative structural property relationship (QSPR) model for estimating flash point of92compounds was developed based on our newly introduced topological electro-negativity indices Yc and WC and path number parameter P3. The model correlation coefficient and standard error for training set in multiple linear regression were0.9923and5.28, respectively. The average absolute error of flash point was only3.86K between experimental values and calculated values, the relative error was1.46%. Furthermore, the model was strictly analyzed by both internal and external validations. The predicted values were obtained in good agreement with experimental values for leave-one-out (LOO) and the training set and validation set. The results showed that this QSPR model was of good stability and powerful prediction ability.4. A newly developed topological vector of atom Yc, equilibrium electro-negativity of atom Xs, molecular structural information parameter [NiH(i=α,β)] and y calibration parameter were used to describe the local chemical microenvironment of63acyclic alcoholic compounds. A quantitative structural spectrum relationship (QSSR) was systematically studied between13C NMR chemical shifts of353carbon atoms and their molecular structure descriptors. By partial least regression (PLS), the statistical results indicated that the model correlation coefficient and standard error were0.9915and2.4827, respectively. And the average absolute error was only2.01ppm between the calculated and experimental chemical shifts for353carbon atoms. To validate the estimation stability for internal samples and the predictive capability for external samples of resulting models, leave-molecule-out cross validation and external validation were performed. Compared with the reported result, not only the number of descriptors employed in this paper was much fewer, but also the calculation was much easier. In addition, a quantitative structure-spectrum relationship model was developed to simulate13C NMR spectra on carbinol carbon atoms for55alcohols. The proposed model, using multiple linear regression, contained four descriptors Yc, Xe,[NiH(i=α,β)] solely from the molecular structure of compounds. The statistical results of the final model showed that R2=0.9824and S=0.8698. The model was statistically significant and showed very good stability to data variation using the leave-one-out cross-validation. The comparison with the other approaches also revealed good behaviors of our method in this QSSR study.5. Two novel molecular structure descriptors based on distance matrix and adjacency matrix, named CN and CT were proposed which characterized branch vertex and molecular structural size of polycyclic aromatic hydrocarbons (PAHs), respectively. A quantitative structure-retention relationship (QSRR) model for estimating gas chromatography retention indexes of100polycyclic aromatic hydrocarbons was constructed by multiple linear regression (MLR). A satisfactory result was obtained that the correlation coefficients in partial least square and cross validation using leave-one-out were0.9970and0.9967, respectively. In order to verify the prediction ability and stability of the model, the samples were divided into70training set and30test set randomly. The result indicated the correlation coefficients of training set and test set were0.9972and0.9968, respectively. The quantitatively calculated results were in agreement with experimental ones basically. The model was compared with recently proposed QSRR models of the similar data. It was found that the present model was all better than relevant achievements in literatures.6. A quantitative structure-electrochemistry relationship (QSER) study of anabolic androgenic steroids had been done on the half-wave reduction potential (E1/2) using quantum and physicochemical molecular descriptors. The descriptors were calculated by semi-empirical method. Successful models were established using partial least square (PLS) regression and back-propagation artificial neural network (BP-ANN). The QSER study results indicated that the descriptors of these derivatives had significant relationship with half-wave reduction potential. The stability and prediction ability of these models were validated using leave-one-out cross-validation and external test set. This study might be helpful in the future successful identification of "real" or "virtual" anabolic androgenic steroids.