Applications of bioinformatics, homology modeling and QSAR to drug design

Drug discovery is a huge endeavor. For every compound marketed, several research-years are expended and millions spent. Recently computer applications have helped hasten the process of drug discovery. This dissertation deals with the different tools and their applications used in the process of drug design and discovery.;In Chapter I, we propose a new method for evaluating distant homologies between a pair of proteins. Using 'Structural Evaluation of Distant Homology' we assess a proposed relationship between the nicotinic acetylcholine receptor (nAChR) and acetylcholinesterase. By mutating a part of the substrate binding domain of acetylcholinesterase into the corresponding aligned region of the nAChR ligand binding domain we support the consistency of the model with known ligand binding data.;In Chapter II, methods such as multi-linear regression, Comparative molecular field analysis and Neural networks are applied to a set of Cholecystokinin-B antagonists. The results of these methods have been compared to each other.;In Chapter III, we have built a homology model of HIV-I env-fs protein based on the 3-D structure of glutathione peroxidase (GPx) proteins. The alignment (Appendix I) was used to create this model, which supports the hypothesis that HIV-1 env-fs is a potentially functional GPx.;Appendix I is a summary of our findings of a protein sequence similarity between GPx family and a hypothetical protein encoded by the env gene in HIV-1.;Appendix II deals with functional analysis of calpain. By protein structure analysis, we predict a possible function of domain III of calpain.;Appendix III describes the work done at an internship. We have constructed 3-D structures to calculate many independent variables and create Building Block Databases (BBDs). An in-house genetic algorithm would be able to choose X number of compounds from a set of N compounds in the BBDs to aid synthetic chemists.