Date of Award

5-26-2021

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Chemistry

First Advisor

Jerome Darsey

Abstract

Alzheimer’s disease (AD) is an irreversible and progressive disease that affects neurons and their connections in parts of the brain specifically the hippocampus and entorhinal cortex. The purpose of this research is to modify current medications used for the treatment of Alzheimer’s Disease utilizing computational modelling. The medications chosen to be modified are the most common drugs used in AD patients to treat mild to severe symptoms. The modifications are concluded to increase the half maximal inhibitory concentration (IC50) value which is the concentration needed for the drug to inhibit a specific biological function. Drug design throughout this research has been done on the computational modelling software Gaussian 09. The work done in this research utilized the values of pIC50 (-log(IC50)) instead of IC50 for the ease of calculations. The pIC50 values for the modified drug molecules are predicted using two methods. First, the functional graph method utilizing the energies and the pIC50 values from literature, resulting in linear correlations that predict pIC50 values for the modified drug molecules. The second method involves using an artificial neural network software, NETS to predict the pIC50 values of modified drug molecules. Four modified drug molecules resulted in promising outcomes in which the pIC50 values were improved. The data obtained shows that computational modelling can be a novel time-saving and a significant preliminary step in drug discovery when compared with traditional organic synthesis approaches.

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