An international team of scientists say they have found a way to make the process of finding a drug to treat COVID-19 much faster using artificial intelligence (AI), according to a news release from the Department of Energy’s Argonne National Laboratory.
Ten organizations, including Argonne National Laboratory, have developed a pipeline of AI and simulation techniques to hasten the discovery of promising drug candidates for COVID-19. The pipeline is named IMPECCABLE, short for Integrated Modeling PipelinE for COVID Cure by Assessing Better Leads.
“With the AI we’ve implemented, we’ve been able to screen four billion potential drug candidates in a matter of a day, while existing computational tools might only realistically screen one to 10 million,” said Thomas Brettin, Strategic Program Manager at Argonne.
At the start of the pipeline, computational techniques are used to calculate the basic properties of billions of molecules. This data is used in the next stage of the pipeline to create machine learning models that can predict how likely it is that a given molecule will bind with a known viral protein. Those found to be most promising are then simulated on high-performance computing systems.
Very large experimental data sets are also being gathered from thousands of protein crystals using X-rays at the Advanced Photon Source (APS), a DOE Office of Science User Facility on Argonne’s campus. The technique they are using to get this data is known as X-ray crystallography. With it, researchers can capture detailed images of viral proteins and their chemical states to improve the accuracy of their machine learning models.
The ultimate goals of the pipeline are to (1) understand the function of viral proteins; (2) identify molecules with a high potential to bind with these proteins and, as a result, block SARS-CoV-2 proliferation; and (3) deliver this insight to drug designers and developers for further research and development.
