Aided Virtual Screening For Alzheimer's Disease And Influenza Virus Inhibitors And Design

Computer-Aided Drug Design(CADD) plays an important role in the discovery of new drugs. It not only makes a rapid progress in the study and discovery new drugs but saves lots of money. CADD has been an important method in new drug discovery. There are four chapters in my thesis research. The detailed contents are as follows:In the first chapter, the knowledges of compute chemistry and the current studies on Alzheimer's disease and neuraminidase inhibitors are introduced.In the second chapter, based on the cheminformatics and the bioinformatics, a new candidate for Alzheimer's disease is screened from the Traditional Chinese Medicines Database(TCMD) using GTS-21 as our template molecule. The new found drug candidate for Alzheimer's disease, Mol 7235, binds with the receptorα7 nAChR better than any other molecules in TCMD. The methods used in my research include similarity search, flexible alignment and molecule docking, The Mol 7235 molecule is different from other molecules as a potential drug candidate and is subject to extensive studies. The results may be very helpful when we modify or design new inhibitors against Alzheimer's disease.In the third chapter, based on the crystal structure of Acetylcholinestemse complexed with Huperzine A(PDB code: 1 vot), flexible alignment and molecule docking were performed and a new molecule Mol 3935 was screened from TCMD, and the best docking conformation of Acetylcholinestemse complexed with Mol 3935 was obtained. According to the structural information and drug design theories, 38 molecules were designed using rational design method based on the conformation of Mol 3935. Some useful observations were obtained by analysis the results of calculations, which could be helpful to design new inhibitors and refine the old inhibitors of Acetylcholinestemse for Alzheimer's disease.In the fourth chapter, based on principles of quantum chemistry and molecular mechanics, 2D-QSAR and 3D-QSAR of a set of neuraminidase inhibitors were studied. Three 2D-QSAR models(one is the best among them) and one 3D-QSAR model were obtained. The predictive result showed that 2D-QSAR model is equally effective with the 3D-QSAR model. The 2D-QSAR model provided more information on the chemical and physical properties for drug molecular design than 3D-QSAR did, on the other hand, the 3D-QSAR model provided better molecular structural information for new drug design than 2D-QSAR did. Therefore, the combination model of 2D-QSAR and 3D-QSAR could help us to design and synthesize new neuraminidase inhibitors for influenza.