On Structural Characterization For Representative Pharmaceuticals And Bioactivity Prediction Through Quantitative Structure-Activity Relationship

Computer-assisted molecular design (CAMD) and quantitative structure-activity relationship (QSAR) are a key fundament and an important procedure of drug design, research and development. Molecular structural characterization heavily decides the success of the QSAR study. In this thesis, a type of novel structural vectorial descriptors, called molecular electronegativity-distance vector (MEDV), which was first developed by our laboratory, have been extended to characterize several complex biological molecules. The main contents and some conclusions are given as follows:1. The original MEDV was used to characterize the chemical structures of 100 polycyclic aromatic hydrocarbons (PAHs) and quantitative relationship between structure and the gas chromatographic retention index has been established by using multiple linear regression (MLR) technique. The correlation coefficients is R =0.9899. The stability and prediction capacity for external samples of the model have been tested by the cross validations method with leave-one-out (LOO), the correlation coefficients is also as high as R cv=0.9856.2. The atomic rooted path vector (ARPV) of various lengths was developed to relate the local circumstance of the carbon atom in 151 alkanes and their chemical shifts of C-13 nuclear magnetic resonance (NMR). For all types of the atoms, the correlation coefficient of atomic modeling equation is 0.944. For primary, secondly, tertiary and quaternary carbon atoms, the correlation coefficients are 0.993, 0.983, 0.961, 0.992, respectively. All of the models were tested to have quite good stability through cross-validation (CV) with the leave-one-out (LOO) procedure.3. The molecular electronegativity-distance vector was extended into several modified forms, and then has been used to express the chemical structures of three kinds of anti-HTV drug molecules including the peptide HTV protease inhibitor, a set of electronic isostere derivatives and cyclic ureas derivatives. Good results could be usefully in the research of new anti-HIV agents with higher anti-HIV activity.4. A novel set of structural descriptors with ten elements, called the hydrogen-association classified molecular electronegativity-distance vector (H-MEDV), was developed to describe the chemical structure of 28 hydropyridine derivatives and reasonable molecular modeling results were achieved with a relatively high correlation coefficient (#=0.924, 0.874, 0.818, respectively) by a multiple linear regression (MLR). For three kinds of biological activities, the models are all have good stability and prediction ability.5. The hydrogen-association classified molecular electronegativity-distance vector (H-MEDV) and a novel descriptor, MHDV, were used to express the structures of 40 aminoquinolines with good relationship (R>0.9). The correlation coefficients (R) between the antiplasmodial activities observed experimentally and the antiplasmodial activities predicted by leave-one-out (LOO) method of cross validations are also higher than 0.8.