Powerful Machine-Learning Approach Helps Identify Hundreds of New Potential COVID-19 Drugs

Scientists at the University of California, Riverside (Riverside, CA, USA), 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.

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. The scientists used small numbers of previously known ligands for 65 human proteins that are known to interact with SARS-CoV-2 proteins and 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. The scientists were thus able to create a database of chemicals whose structures were predicted as interactors of the 65 protein targets and also evaluate the chemicals for safety.

The scientists used their machine learning models to screen more than 10 million commercially available small molecules from a database comprised 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.

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

“Our database can serve as a resource for rapidly identifying and testing novel, safe treatment strategies for COVID-19 and other diseases where the same 65 target proteins are relevant,” said Joel Kowalewski, a graduate student in Ray’s lab. “While the COVID-19 pandemic was what motivated us, we expect our predictions from more than 10 million chemicals will accelerate drug discovery in the fight against not only COVID-19 but also a number of other diseases.”

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University of California, Riverside