In order to find a drug that can stop the SARS-COV-2 virus which causes COVID-19, scientists have to screen billions of molecules for the right combination of properties. The process is usually risky, slow and often takes several years.
However, an international team of scientists say that they have found a way to make the process 50,000 times faster using AI. Ten organizations including the U.S Department of Energy’s (DOE) Argonne National Laboratory https://www.anl.gov have developed a pipeline of AI and simulation techniques to hasten the discovery of promising drug candidates for COVID-19.
The pipeline is named Integrated Modeling PipelinE for COVID Cure by Assessing Better Leads or referred to as IMPECCABLE. IMPECCABLE integrates multiple techniques for data processing, physics-based modeling, and simulation and machine learning, where a form of AI then uses patterns in data to generate predictive models.
“With the pipeline, it is possible to screen huge numbers of molecules automatically, and dramatically increase the chance of finding a likely candidate,”, said Ian Foster, Director of Argonne’s Data Science and Learning Division.
“With the implementation of AI, we have 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.
The ultimate goal for the IMPECCABLE pipeline is to 1) understand the function of viral proteins, 2) identify molecules with a high potential to bind with these proteins and as result block SARS-COV-2 proliferation, and 3) deliver this insight to drug designers and developers for further research and development.