3 - Aryl Quinazolinone Tetrahydroisoquinoline Class Of Selective Er Modifier Of Qsar And Docking Study

Quantitative structure-activity relationship is an active research field worldwide. It is a frontal task in agricultural chemistry, environmental chemistry and medicinal chemistry. Especially in environmental toxicology evaluation and computer-aided drug design,QSAR is gaining popularity and wide applications. This dissertation introduced artificial neural network, support vector machine, comparative molecular field analysis, comparative molecular similarity indices analysis and hologram quantitative structure-activity relationship analysis combined with docking studies to study the structure-activity relationships of 3-arylquinazolinethione derivatives and tetrahydroisoquinoline derivatives as ERβand ERαinhibitors, respectively. And finally we found out the important factors determining the activity, which is very helpful for structure modification and discovery of new active compounds.In the first chapter of the paper, a review of QSAR and docking methods, progress and applications is presented.In chapters 2 and 3 of the dissertation, quantum, topological and hydrophobic descriptors were combined to study the quantitative structure- activity relationship of 42 3-arylquinazolinethione derivatives as selective estrogen receptor modulators. Principal component analysis was chosen to find out 7 principal components among the 35 descriptors. Three methods including Multiple Linear Regression (MLR), Generalized Regression Neural Network (GRNN) and Support Vector Machine (SVM), were used to build QSAR models respectively. Comparison of the results obtained from the three models showed that the SVM and GRNN methods exhibited better performance than MLR method. SVR method got better results for the prediction of the test set, which showed the better generalization of the SVM method.Chapter four discussed the docking studies and 3D-QSAR of 45 3-arylquinazolinethione derivatives as selective estrogen receptor modulators by CoMFA method. Understanding intermolecular interactions of 3-Arylquinazolinethione derivatives with ERβand ERαwas achieved by performing molecular docking, 3D-QSAR, and 3D-QSSR analyses. The use of molecular conformations of the compounds derived from molecular docking led to satisfactory 3D-QSAR and 3D-QSSR models (with high cross-validation correlation coefficient q2 and conventional correlation coefficient R2 values) for predicting the inhibitory activity against ERβand the selectivity against ERα. The high q2 and R2 values, along with further testing, indicated that the obtained QSAR and QSSR models were valuable in predicting both the inhibitory activity and selectivity of 3-Arylquinazolinethione derivatives against these protein targets. A set of 3D contour plots drawn based on the 3D-QSAR and QSSR models revealed moderate bulky groups with positive-rich charges in 5-position and small bulk groups in 2-position should improve the inhibitory activity and selectivity by modifying structures of the compounds. It can be concluded that both the steric and electrostatic factors should be considered appropriately for designing novel ERβinhibitors with higher inhibitory activity and selectivity. It is helpful to study the mechanism and identify new potential new modulators.In chapter five, two- and three-dimensional quantitative structure-activity relationships have been studied on a series of tetrahydroisoquinoline derivatives by comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA) and hologram quantitative structure-activity relationship (HQSAR). Different alignments were used in order to investigate the effects on the CoMFA and CoMSIA models. Different combinations of fields and different fragment parameters were also applied to explore the effects on the CoMSIA and HQSAR models. For the CoMFA method, the alignment method of"RMS and Docking Results"resulted the best model. The q2 value was 0.779 with principal components of 3, the noncross- validated squared coefficient was 0.932. For the CoMSIA method, the best model was established by the alignment of"Docking Results"and three fields (steric, electrostatic and the hydrophobic fields) with the optimal number compotent of 3, q2 of 0.825 and R2 of 0.949. And the contributions of the steric, electrostatic and the hydrophobic fields were 0.265, 0.421 and 0.315, respectively. It can be concluded that the activity of these inhibitors was mainly dependent on the steric, electrostatic and hydrophobic interactions. The comparison of the contributions of the three fields suggests that the hydrophobic interactions play a much important role in the interactions between the ligands and the ERαrecptor. For the HQSAR method, the fragment distinction of combination atom type and bond type resulted in the best model. The q2 value was 0.831 with principal components of 5, the noncross- validated squared coefficient was 0.956. Three QSAR methods are used to investigate the relationship between the structures of 21 modulators of ERαand their activities in order to design new potent ERαmodulators.