Utilizing Computational Chemistry to Crowdsource a Treatment for SARS-CoV-2
Advisor(s)
Dr. John Thurmond; Illinois Mathematics and Science Academy
Discipline
Chemistry
Start Date
21-4-2021 8:50 AM
End Date
21-4-2021 9:05 AM
Abstract
In December 2019, a novel coronavirus was detected in the Chinese city of Wuhan, Hubei Province. Nearly affecting all facets of life in China, this virus soon spread to the rest of the world, and on March 11, 2020, the World Health Organization declared a worldwide pandemic for SARS-CoV-2. Due to this unprecedented situation, medical researchers were left racing to find a viable treatment for the novel coronavirus through both conventional and innovative technologies. Likewise, this study utilized fragment-based drug design, a growing field within computational chemistry, to aid in the search for a viable treatment. To accomplish this, SEESAR was used to collect chemical structures, and their respective binding affinities, based on known SARS-CoV-2 Mpro drug-fragments. These structures were run through ADMET data collection sites to gather data pertaining to drug toxicity, protein inhibition, and synthetic accessibility. As a result, the top compounds resulting from this study were submitted to COVID Moonshot, a global crowdsourced research effort led to find a viable oral treatment to SARS-CoV-2. Currently, these compounds are under review for potential biological assay.
Utilizing Computational Chemistry to Crowdsource a Treatment for SARS-CoV-2
In December 2019, a novel coronavirus was detected in the Chinese city of Wuhan, Hubei Province. Nearly affecting all facets of life in China, this virus soon spread to the rest of the world, and on March 11, 2020, the World Health Organization declared a worldwide pandemic for SARS-CoV-2. Due to this unprecedented situation, medical researchers were left racing to find a viable treatment for the novel coronavirus through both conventional and innovative technologies. Likewise, this study utilized fragment-based drug design, a growing field within computational chemistry, to aid in the search for a viable treatment. To accomplish this, SEESAR was used to collect chemical structures, and their respective binding affinities, based on known SARS-CoV-2 Mpro drug-fragments. These structures were run through ADMET data collection sites to gather data pertaining to drug toxicity, protein inhibition, and synthetic accessibility. As a result, the top compounds resulting from this study were submitted to COVID Moonshot, a global crowdsourced research effort led to find a viable oral treatment to SARS-CoV-2. Currently, these compounds are under review for potential biological assay.