In Silico Methodologies for Selection and Prioritization of Compounds in Drug Discovery

Abstract

In the first part of the thesis, the objective was to formulate a computational workflow that can be used to prioritize compounds of interest from a primary screen hit list for re-confirmation screening. A computational methodology based on the Random Forest Clustering method was presented. In the second part, the concept of Correlation Rules was applied with the aim of uncovering substructures that are not only well represented among known potent inhibitors but are also unrepresented among known inactive compounds and vice versa. In the third part, the objective was to identify small molecules that are potential inhibitors of a particular therapeutic target in the search for a treatment for dengue. A virtual screening protocol was formulated. In the final part, a novel application of the Taguchi Method, which is an approach based on Design of Experiments (DoE), was investigated for lead optimization and structure-activity relationship development of compounds.