Degree

Doctor of Philosophy (PhD)

Department

Chemistry

Document Type

Dissertation

Abstract

Natural products, secondary metabolites that have long served as an attractive source of biologically active compounds1 continue to play a critical role in modern drug discovery. These privileged compounds have historically been an attractive source of novel anti-cancer therapeutics. Despite their therapeutic potential, the structural complexity and physicochemical properties of many natural products present substantial challenges in lead identification, optimization, and clinical advancement. This dissertation evaluates strategies for developing novel anti-cancer agents by integrating computational analysis and synthetic organic chemistry.

Chapter 1 describes a detailed background on the role of natural products in drug discovery, emphasizing their structural diversity, biological relevance, and the challenges that hinder their development. This chapter examines the development of a novel AI-assisted chemical informatics training tool to evaluate physicochemical properties for compound optimization. Chapter 2 describes the total synthesis of berkeleyamide A (BA) and uses molecular modeling techniques to develop a comprehensive derivative library. Chapter 3 focuses on the evaluation and synthesis of cholesterol endoperoxide (CPO) and its derivatives, using biotin pull-down assays to identify a novel biological target in colorectal cancer cell models.

This work highlights the integration of computational tools with synthetic chemistry to enhance the evaluation and development of natural products as viable drug candidates. The approaches presented herein aim to improve early-stage decision-making in drug discovery while providing a cohesive framework that bridges foundational background, computational analysis, and biological evaluation.

Date

5-11-2026

Committee Chair

Fatima Rivas

LSU Acknowledgement

1

LSU Accessibility Acknowledgment

1

Available for download on Thursday, May 10, 2029

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