Design of COVID-19 Antivirals Using Computer Modeling
Advisor(s)
Dr. John Thurmond; Illinois Mathematics and Science Academy
Discipline
Chemistry
Start Date
21-4-2021 10:05 AM
End Date
21-4-2021 10:20 AM
Abstract
The discovery and development of effective antiviral drugs for COVID-19 are urgent and ongoing. An initiative to contribute to this process is COVID Moonshot project. The aim of the project is to rapidly develop easily manufacturable antiviral drugs that can inhibit the SARS-CoV-2 main protease. To provide leads for the intractable biological target in COVID, we used fragment-based drug discovery which identifies low-molecular weight ligands that bind to biologically important macromolecules. Our group started with the fragment x1086 from the COVID Moonshot Consortium and successfully designed new molecules in SeeSAR, a 3D modeling software platform. We designed the molecules from different bonds from the fragment and calculated their estimated affinities and other physicochemical properties. We selected the compounds with the best estimated affinities from the 341 compounds we designed in SeeSAR from each bond and entered them into swissADME and ADMETSAR, websites that predict physicochemical descriptors and absorption, digestion, metabolism, excretion, and toxicity (ADMETox) parameters. These websites allowed us to see if our best binding affinity molecules were druglike and had good ADMETox properties. Specifically, we looked at Lipinski’s rules and human ether-à-go-go related genes (hERG inhibition). We submitted our best eight compounds, which demonstrated the best affinity and drug-like properties, from the 341 molecules that were designed to the COVID Moonshot Initiative for further testing and drug development.
Design of COVID-19 Antivirals Using Computer Modeling
The discovery and development of effective antiviral drugs for COVID-19 are urgent and ongoing. An initiative to contribute to this process is COVID Moonshot project. The aim of the project is to rapidly develop easily manufacturable antiviral drugs that can inhibit the SARS-CoV-2 main protease. To provide leads for the intractable biological target in COVID, we used fragment-based drug discovery which identifies low-molecular weight ligands that bind to biologically important macromolecules. Our group started with the fragment x1086 from the COVID Moonshot Consortium and successfully designed new molecules in SeeSAR, a 3D modeling software platform. We designed the molecules from different bonds from the fragment and calculated their estimated affinities and other physicochemical properties. We selected the compounds with the best estimated affinities from the 341 compounds we designed in SeeSAR from each bond and entered them into swissADME and ADMETSAR, websites that predict physicochemical descriptors and absorption, digestion, metabolism, excretion, and toxicity (ADMETox) parameters. These websites allowed us to see if our best binding affinity molecules were druglike and had good ADMETox properties. Specifically, we looked at Lipinski’s rules and human ether-à-go-go related genes (hERG inhibition). We submitted our best eight compounds, which demonstrated the best affinity and drug-like properties, from the 341 molecules that were designed to the COVID Moonshot Initiative for further testing and drug development.