Date of Publication

2-2021

Document Type

Master's Thesis

Degree Name

Master of Science in Chemistry

Subject Categories

Chemistry

College

College of Science

Department/Unit

Chemistry

Thesis Adviser

Derrick Ethelbhert C. Yu

Defense Panel Member

Rafael A. Espiritu
Raymond S. Malabed
Frumencio F. Co

Abstract/Summary

The widespread infection caused by the 2019 novel corona virus (SARS-CoV-2) has initiated global efforts to search for antiviral agents. Drug discovery is the first step in the development of commercially viable pharmaceutical products to deal with novel diseases. In an effort to accelerate the screening and drug discovery workflow for potential SARS-CoV-2 protease inhibitors, a machine learning model that can predict the binding free energies of compounds to the SARS-CoV-2 main protease is presented. The regression model, which was trained and tested on 226 compounds demonstrates reliable prediction performance (r2 test = 0.81, RMSE test = 0.43), while only requiring five topological descriptors.

The externally validated model can help conserve and maximize available resources by limiting biological assays to compounds that yielded favorable outcomes from the model. Moreover, the model was used to design molecular modifications on the compound rutin to enhance its binding activity with the main protease. The emergence of highly infectious diseases will always be a threat to human health and development, which is why the development of computational tools for rapid response is very important.

Abstract Format

html

Language

English

Format

Electronic

Physical Description

ix, 120 leaves

Keywords

COVID-19 (Disease); Antiviral agents

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Embargo Period

4-27-2022

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