Target Identification And Quantitative-Structure Activity Relationship Study Of Selective Small Molecular Inhibitors Of Fibroblast Growth Factor Receptor 1 (FGFR1)

Computer-aided drug design (CADD) has become an essential tool in the process of current drug development. This thesis mainly introduces the development and applications of some key techniques in CADD and gives some specific introduction to the research work in the fields of In Silico drug target identification, studies on Structure-Activity Relationship (SAR) and Quantitative Structure-Activity Relationship (QSAR) of drugs.The first chapter of the thesis is the foreword, which introduces the background and advancement of CADD. It mainly focuses on the molecular docking, pharmacophore, QSAR and In Silico drug target identification.The second chapter of the thesis describes our research work in target identification and SAR studies of FGFR1 small molecular selective inhibitors. We predict the drug target of acenaphtho-[1,2-b] pyrrole derivatives with moderate antiproliferacive activity of tumor cells by an inverse pharmacophore mapping approach-PharmMapper. In accordance with our previously calculated results, ELISA experiment also proves that acenaphtho-[1,2-b] pyrrole derivates are novel tyrosine kinase inhibitors. They exert potent inhibition against FGFR1 with high selectivity. Based on the predicted binding modes of inhibitors at the ATP binding site of FGFR1 kinase domain obtained by molecular docking method, we study the SAR of some new synthesized acenaphtho-[1,2-b] pyrrole derivates and the inhibition selectivity among different subtypes. This is a good application example to demonstrate drug target identification using computer aided approaches. Moreover, the results will be very important for studying novel FGFR1 inhibitors, which are used for cancer therapy and are also very meaningful to study the mechanisms of FGFRs related diseases.In the third chapter of the thesis, we design and complement a procedure, which combines GA with a statistical method PLS in Python language and apply it to further understand the QSAR of antiproliferative effect of acenaphtho-[1,2-b] pyrrole FGFR1 inhibitors. Finally, we obtain some reliable QSAR models of antiproliferative effect of acenaphtho-[1,2-b] pyrrole FGFR1 inhibitors and their properties, which are not only more benificial for QSAR study but also provide us with helpful information for further structure optimization.The last chapter of the thesis sum up the whole work, disadvantanges and outlook of CADD.