| Cancers are malignant tumors which have been plaguing humans for a long time.Cancer cells are difficult to cured due to the fact that they are not controlled by human regulatory systems(as normal cells are,besides cancer cells).In addition,cancers cells can not only damage the tissues around them,they can also spread within human body to damage other tissues.Therefore,search for targeted anti-tumor drugs with low side effects has become our goal.Tumor angiogenesis inhibitors have the characteristics of targeting tumor blood vessels,small side effects,small doses,high efficacy,and low drug resistance,which have the potential to become a research hotspot in the anti-cancer drug design process.With the application of computer-aided drug design method,the thesis analyzed protein tyrosine phosphatase(HPTPP)vascular inhibitors,matrix metalloproteinase 2(MMP-2)vascular inhibitors,vascular endothelial growth factors receptors 2(VEFGR-2)vascular inhibitors,quinazoline vascular inhibitors,and thymidine phosphorylase(TP)vascular inhibitors to conduct quantitative structure-activity relationship(QSAR)study,design promising compounds,and study the binding mode of compounds with target proteins.It provides a theoretical basis for the further development of anticancer drugs.The specific studies on five kinds of vascular inhibitors are as follows:1.The Topomer CoMFA method is based on the R group search for 3D-QSAR modeling construction in a series of HPTPβ derivatives.Stable and ideal QSAR models were built.This research proved that the 3D model established by this method has a good predictive ability.Based on the established model,five small molecules were designed by using topomer search and screening in the ZINC database.Subsequently,with the application of molecular docking technology,the binding mechanism between small molecules of the inhibitors and target proteins was explored.The final results suggested that the newly-designed five small drug molecules could have strong interactions with proteases by forming multiple stable hydrogen bonds.The results of the QSAR study can provide a theoretical reference for the synthesis of HPTPβ vascular inhibitors.2.35 MMP-2 inhibitors were used to build the topomer CoMFA model.The non-cross validation coefficient r2,and cross-validation correlation coefficient q2 of the optimal model are 0.967 and 0.881,respectively.On the basis of the established model,five new compounds were screened in the Zinc database by using topomer search.The results showed that the optimal model has good stability and prediction ability.In addition,the confirmation of the newly designed compounds with target proteins is relatively stable.Therefore,the R-group search based on topomer search technology can provide a basis for the design of new anti-cancer drugs such as MMP-2 vascular inhibitors.3.The topomer CoMFA technology and molecular holographic quantitative structure-activity(HQSAR)technology were performed to study a series of VEFGR-2 derivatives.The models constructed by the two methods showed good predictability and reliable stability for VEFGR-2 compounds.On the basis of the established model,the newly designed compounds were screened in the Zinc database by using topomer search.To predict the activities of the newly-designed compounds.Eight of the designed compounds were ideal for subsequent work.Finally,molecular docking experiments were conducted into the retained compounds.The results showed that the results of the two QSAR studies can provide theoretical references for the new drug research of VEFGR-2 vascular inhibitors.4.The CoMFA,CoMSIA,and HQSAR methods were used to explore the structural requirements of a series of quinazoline derivatives with molecular comparisons.The modeling data showed that the models built on the three methods have ideal predictive power for quinazoline compounds.Among them,the cross-validation coefficient q2 is 0.621 and the non-interactive validation coefficient r2 is 0.959 in the CoMFA model;the best cross-validation coefficient q2 is 0.522 and the non-interactive validation coefficient r2 is 0.961 in the CoMSIA model;The most ideal HQSAR model shows the cross-validation coefficient q2 is 0.533,the non-cross-validation coefficient r2 is 0.871,and the optimal holographic length L is 199.According to these data,the three optimal models can be used as the basis for subsequent work.In addition,molecular docking technology was used to explore the binding modes between a series of compounds and target proteins.The results showed that hydrogen bonding affinity provided a great contribution to the binding affinity between quinazoline compounds and target proteins.These QSAR research work has laid the foundation for the further design of more active quinazoline anticancer drugs.5.The 3D-QSAR model was established at 30 thymidine phosphorylase(TP)derivatives using CoMFA and CoMSIA methods.The results showed that the cross-validation coefficient q2 is 0.697 and the non-cross-validation coefficient r2 is 0.920 in the CoMFA model;The best cross-validation coefficient q2 is 0.692 and the non-cross-validation coefficient r2 is 0.912 in the CoMSIA model.The statistical parameters show that the two optimal models built can be used as the basis for subsequent work.Five new small molecules of TP derivatives were designed based on the equipotential diagram.In addition,a series of compounds and receptors were explored employing molecular docking technology.The results showed that hydrogen bonding provided a greater contribution to the binding of TP compounds with target proteins. |
