NCATS data support models to screen molecules for COVID-19 therapeutic potential

NCATS data support models to screen molecules for COVID-19 therapeutic potential

May 12, 2021

Using data from the National Center for Advancing Translational Sciences (NCATS), researchers from two universities have created a tool to help drug researchers quickly identify molecules capable of disarming the virus that causes COVID-19 before it invades human cells or disabling it in the early stages of the infection.

In a paper published May 3 in Nature Machine Intelligence, the researchers introduced REDIAL-2020, an open source online suite of computational models that will help scientists rapidly screen small molecules for their potential COVID-fighting properties.

A team from the University of New Mexico (UNM) and another from the University of Texas at El Paso started work on the REDIAL-2020 tool last spring after NCATS scientists released data from their own COVID drug repurposing studies.

"Becoming aware of this, I was like, 'Wait a minute, there's enough data here for us to build solid machine learning models,'" said Tudor Oprea, MD, PhD, of UNM.

The results from NCATS laboratory assays gauged each molecule's ability to inhibit viral entry, infectivity and reproduction, such as the cytopathic effect - the ability to protect a cell from being killed by the virus.

Biomedicine researchers often tend to focus on the positive findings from their studies, but in this case, the NCATS scientists also reported which molecules had no virus-fighting effects. The inclusion of negative data actually enhances the accuracy of machine learning, Oprea said.

"The idea was that we identify molecules that fit the perfect profile," he said. "You want to find molecules that do all these things and don't do the things that we don't want them to do."

Researchers will likely devise a multi-drug cocktail that attacks the virus on multiple fronts.

REDIAL-2020 is based on machine learning algorithms capable of rapidly processing huge amounts of data and teasing out hidden patterns that might not be perceivable by a human researcher. Oprea's team validated the machine learning predictions based on the NCATS data by comparing them against the known effects of approved drugs in UNM's DrugCentral database.

In principle, this computational workflow is flexible and could be trained to evaluate compounds against other pathogens, as well as evaluate chemicals that have not yet been approved for human use, Oprea said.

"Our main intent remains drug repurposing, but we're actually focusing on any small molecule," he said. "It doesn't have to be an approved drug. Anyone who tests their molecule could come up with something important."

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