Novel Pipeline of AI and Simulation Tools Could Make Process of Screening Drug Candidates for COVID-19 50,000 Times Faster

The process is usually risky and slow, often taking several years. An international team of scientists from 10 organizations, including the US Department of Energy’s (DOE) Argonne National Laboratory (Lemont, IL, USA), have found a way to make the process 50,000 times faster using artificial intelligence (AI). The team has developed a pipeline of AI and simulation techniques, named IMPECCABLE, short for Integrated Modeling PipelinE for COVID Cure by Assessing Better Leads to hasten the discovery of promising drug candidates for COVID-19.

IMPECCABLE integrates multiple techniques for data processing, physics-based modeling and simulation, and machine learning, a form of AI that uses patterns in data to generate predictive models. 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’re 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 understand the function of viral proteins; identify molecules with a high potential to bind with these proteins and, as a result, block SARS-CoV-2 proliferation; and deliver this insight to drug designers and developers for further research and development.

“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.

“Unlike the traditional approach, where you rely on the scientist to think really hard and, based on what they know, come up with ideas for a molecule, with our pipeline you can screen huge numbers of molecules automatically, dramatically increasing your chance of finding a likely candidate,” said Ian Foster, director of Argonne’s Data Science and Learning division.

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Argonne National Laboratory