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Scientists create new tools to quickly screen molecules for anti-Covid properties

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Researchers at the University of New Mexico Center for Health Sciences have created an open source online suite of computational models to help scientists quickly screen small molecules for potential Covid-fighting properties.

A year after the Covid-19 pandemic, mass vaccination began to increase the attractive prospects for herd immunity and eventually suppressed or stopped the spread of SARS-CoV-2. But what if herd immunity is not fully achieved, or if the mutant virus produces highly virulent variants that reduce the benefits of vaccination?

These questions underscore the need for effective treatment for people who continue to get sick with the coronavirus. While some existing medications show some benefits, there is an urgent need to find new treatments.

Scientists have unique tools that help drug researchers quickly identify molecules that can disarmament the virus before it invades human cells or neutralizes the virus in the early stages of infection. created.

Researchers introduced REDIAL-2020 through the findings published in the journal Nature Machine Intelligence. This is an open source online suite of computational models that helps scientists quickly screen small molecules for potential Covid-fighting properties.

“To some extent, this replaces (laboratory) experiments,” says Oprea, head of translation informatics at UNM School of Medicine. “It narrows the areas that people need to focus on. That’s why we published it online for everyone to use.”

UNM’s Oprea team and another group led by Suman Sirimulla, PhD at the University of Texas at El Paso, REDIAL-last spring after scientists at the National Center for Advancing Translational Sciences (NCATS) released their own data. We have started working on the 2020 tools. Covid’s drug diversion research.

“When I realized this, I thought,’Wait a minute. We have enough data here to build a solid machine learning model,” says Oprea. The results of the NCATS lab assay measured the ability of each molecule to inhibit virus invasion, infectivity, and reproduction, including the cytopathic effect, that is, the ability to prevent cells from being killed by the virus.

Biomedicine researchers often tend to focus on positive findings from their work, but in this case NCATS scientists also reported which molecules were ineffective in fighting the virus. Including negative data actually improves the accuracy of machine learning, Oprea says.

“The idea was to identify a molecule that fits the perfect profile,” he says. “You want to find a molecule that does all this and doesn’t do what we don’t want them to do.”

Coronavirus is a wise enemy, says Oprea. “I don’t think there’s a drug that makes everything T-matched.” Instead, researchers could devise a multi-drug cocktail that attacks the virus in multiple ways. “It’s back to one-two punch,” he says.

REDIAL-2020 is based on machine learning algorithms that can quickly process large amounts of data and extract hidden patterns that human researchers may not recognize. Oprea’s team validated machine learning predictions based on NCATS data and compared them to the known effects of drugs approved in UNM’s DrugCentral database.

In principle, this computational workflow is flexible and can be trained not only to evaluate compounds against other pathogens, but also to evaluate chemicals that have not yet been approved for human use. Oprea says.

“Our main purpose remains drug repositioning, but in reality we focus on every small molecule,” he says. “It doesn’t have to be an approved drug. Anyone testing their molecule can come up with something important.”

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