Scientists identify hundreds of drug candidates to treat COVID-19

Through a new drug discovery pipeline, scientists at the University of California, Riverside, have used machine learning to identify hundreds of new potential drugs that could help treat COVID-19, the disease caused by the novel coronavirus SARS-CoV-2.

The drug discovery pipeline is a type of computational strategy linked to artificial intelligence — a computer algorithm that learns to predict activity through trial and error, improving over time.

With no clear end in sight, the COVID-19 pandemic has disrupted lives, strained healthcare systems and weakened economies. Efforts to repurpose drugs such as Remdesivir have achieved some success. A COVID-19 vaccine could be months away, though it’s not guaranteed.

Because of that, drug candidate pipelines such as this one represent a first step toward the discovery of new drugs to treat the virus, with a high priority on existing, FDA-approved drugs that target one or more human proteins important for viral entry and replication.


The scientists were able to create a database of chemicals whose structures were predicted as interactors of 65 protein targets, which are implicated in many diseases, including cancers. They also evaluated the chemicals for safety.

The team then used machine learning models to screen more than 10 million commercially available small molecules from a database of 200 million chemicals, and identified the best-in-class hits for the 65 human proteins that interact with SARS-CoV-2 proteins.

Taking it a step further, they identified compounds among the hits that are already FDA approved, such as drugs and compounds used in food. They also used the machine learning models to compute toxicity, which helped them reject potentially toxic candidates. This helped them prioritize the chemicals that were predicted to interact with SARS-CoV-2 targets. 

Their method allowed them to not only identify the highest scoring candidates with significant activity against a single human protein target, but also find a few chemicals that were predicted to inhibit two or more human protein targets.

The scientists argue that their computational strategy for the initial screening of vast numbers of chemicals has an advantage over traditional cell-culture-dependent assays that are expensive and can take years to test. They’re looking for funding and collaborators to move toward testing cell lines, animal models, and eventually clinical trials.


The federal government is in a race to have a COVID-19 vaccine on the market by early next year. The U.S. Department of Health and Human Services’ Operation Warp Speed, for example, is an initiative that aims to deliver 300 million doses of a safe, effective vaccine for COVID-19 by the peak of the next flu season. It’s part of a broader strategy to accelerate the development, manufacture and distribution of coronavirus vaccines, therapeutics and diagnostics – collectively referred to by HHS as “countermeasures.”

OWS intends to achieve this goal by investing in and coordinating countermeasure development, in part by partnering with components of HHS, including the Centers for Disease Control and Prevention, the Food and Drug Administration, the National Institutes of Health, and the Biomedical Advanced Research and Development Authority.

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