Computational Development of Macrocyclic Drug Hits and Synthetic Strategy towards Peptide Macrocyclization Public

Abstract

Protein-protein interactions (PPIs) are often considered "undruggable" targets because they are difficult to inhibit by conventional small molecule drugs. Macrocyclic peptides have emerged as a promising class of therapeutic agents because of their capacity to resemble PPIs, along with several important pharmacological advantages. In particular, macrocyclization is considered as a strategy to improve the oral bioavailability of drug candidates. Therefore, it has also been applied to non-peptide molecules to enhance their druggability. Herein, we will describe our idea and efforts to develop machine learning models to identify potential non-peptide macrocyclic drug hits from a randomly generated library of macrocyclic compounds. This will minimize the time and cost of purchasing, synthesizing, and screening a large library of molecules and accelerate the current drug development process. In addition, we will report on our progress towards peptide macrocyclization via cobalt catalysis. This new approach will diversify the toolkit through the use of an environmentally friendly and earth-abundant transition metal. The future integration of the two divisions, computational and synthetic chemistry, will certainly advance our understanding of the synthesis and applications of macrocycles in the pharmaceutical industry.

Table of Contents

Table of Contents

Chapter 1 Introduction to Macrocycles and Drug Development

Chapter 2 Computational Construction of Macrocyclic Libraries Towards Drug Design

2.1 In Silico Research in Drug Development

2.2 Results and Discussion

2.3 Conclusion and Future Directions

Chapter 3 Synthetic Peptide Macrocyclization via 1,2-Carboamidation

3.1 Peptide Macrocyclization through C-H functionalization

3.2 Results and Discussion

3.3 Conclusion and Future Directions

Chapter 4 Conclusion: Integrating the Computational and Synthetic Tools

References

Supplemental Information

1. General Information

2. Construction of Libraries of Macrocycles

3. Experimental Section for Synthetic Peptide Macrocyclization

4. Supplemental References

About this Honors Thesis

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School	Emory College
Department	Chemistry
Degree	B.S.
Submission	Honors Thesis
Language	English
Research Field	Chemistry, Organic
Mot-clé	macrocycles C-H functionalization machine learning
Committee Chair / Thesis Advisor	Simon Blakey, Emory University
Committee Members	Didem Uca, Emory University John Heemstra Jr., Emory University

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