- By Lynda De Widt
Using AI to screen 30 million drug candidates against SARS-CoV-2
Mayo Clinic researchers and collaborators used computer simulation and artificial intelligence to virtually screen 30 million drug candidates that may block SARS-CoV-2, the virus that causes COVID-19. In a paper published in Biomolecules, researchers accelerated drug discovery to identify the most promising targets for additional study. They are interested in finding new treatments for COVID-19.
"We used a multidrug platform to screen the drug candidates," says Thomas Caulfield, Ph.D., a molecular neuroscientist at Mayo Clinic and senior author of the paper. "We looked at FDA-approved and clinically tested drugs, as well as novel compounds. By using the computational power of advanced technology, we could determine the best drug, gleaned from a compound library, for further investigation."
The research was conducted using computer simulation, called in silico screening ― meaning in silicon, or in the computer ― and validated using biological experiments with live virus. This type of research uses digital databases and mathematical constructs to identify potentially useful drug compounds. Other types of research take place in cell lines ― what's called in vitro ― or in living organisms, such as mice or humans ― what's called in vivo.
Researchers started with 30 million drug compounds. Virtual screening tools made predictions about the behavior of the various drug compounds, modeling how they would interact with biological targets on SARS-CoV-2 particles. In silico screening narrowed the focus to 25 compounds. Then for deeper analysis and testing in a laboratory, the researchers conducted a pilot study on the 25 compounds against infectious SARS-CoV-2 in human cell cultures. They later tested for a common problem with drugs: toxicity.
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