Date of Award

2024-05-01

Degree Name

Doctor of Philosophy

Department

Chemistry

Advisor(s)

Lela VukoviÄ?

Abstract

Alzheimer's disease is a debilitating brain disorder that affects memory, thinking, and behavior. It is the most prevalent form of dementia and the seventh leading cause of death in the United States. As, researchers have identified biomarkers that can indicate the presence of the disease many years before the first symptoms appear, so there is an opportunity for early detection and treatment follow-ups, which could significantly improve the quality of life for those affected by this disease.This dissertation investigates the relationship between molecular structure and optical properties of donor-bridge-acceptor (DBA) fluorescent molecular probes designed for detecting Amyloid-β and p-Tau protein aggregates characteristic biomarkers of Alzheimer's disease from blood samples. Employing computational methods, including density functional theory (DFT) and time-dependent density functional theory (TD-DFT), we studied the behavior of excited states and absorption characteristics of these molecular probes. The first focus of our study was to understand the nature of the excited state of DBA fluorescent molecular probes using TD-DFT with implicit solvation methods. The direct correlation between experimental and calculated absorption energies validated the utility and efficacy of our computational approach in accurately capturing electronic and structural changes, and absorption properties. The investigation was complemented by the systematic study of 138 fluorescent molecular probes to identify structure-activity relationships for absorption energies. The influence of different structural modifications was considered, including the linker (bridge), electron-withdrawing, and electron-donating group variation. Furthermore, our exploration of machine learning techniques demonstrates promising capabilities in predicting absorption energies for DBA fluorescent probes. In summary, this dissertation contributes to advancing molecular design strategies for Alzheimer's disease detection by uncovering fundamental principles governing the optical properties of fluorescent probes. The integration of computational methodologies with experimental insights provided valuable guidance for the rational design of these molecular probes. The application of machine learning techniques shows promise in accelerating the screening and optimization of fluorescent probes, advancing efforts toward the early diagnosis and treatment of Alzheimer's disease.

Language

en

Provenance

Received from ProQuest

File Size

130 p.

File Format

application/pdf

Rights Holder

Gabriela Elizabeth Molina Aguirre

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