100s Of Rx ID’ed As Possible Covid-19 Treatments

Aug 12, 2020 |

University of California – Riverside – 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, or SARS-CoV-2.

“There is an urgent need to identify effective drugs that treat or prevent COVID-19,” said Anandasankar Ray, a professor of molecular, cell, and systems biology who led the research. “We have developed a drug discovery pipeline that identified several candidates.”

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 health care systems, and weakened economies.

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Efforts to repurpose drugs, such as Remdesivir, have achieved some success. A vaccine for the SARS-CoV-2 virus could be months away, though it is not guaranteed.

Ray said.

“As a result, drug candidate pipelines, such as the one we developed, are extremely important to pursue as a first step toward systematic discovery of new drugs for treating COVID-19.

“Existing FDA-approved drugs that target one or more human proteins important for viral entry and replication are currently high priority for repurposing as new COVID-19 drugs.

“The demand is high for additional drugs or small molecules that can interfere with both entry and replication of SARS-CoV-2 in the body. Our drug discovery pipeline can help.”

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Joel Kowalewski, a graduate student in Ray’s lab, used small numbers of previously known ligands for 65 human proteins that are known to interact with SARS-CoV-2 proteins.

He generated machine learning models for each of the human proteins.

“These models are trained to identify new small molecule inhibitors and activators—the ligands—simply from their 3-D structures,” Kowalewski said.

Kowalewski and Ray were thus able to create a database of chemicals whose structures were predicted as interactors of the 65 protein targets. They also evaluated the chemicals for safety.

“The 65 protein targets are quite diverse and are implicated in many additional diseases as well, including cancers,” Kowalewski said … Read more. 

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