UC Riverside researchers used a powerful machine-learning approach to screen millions of chemicals to find suitable candidates
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. 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.
“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.”
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This innovation describes the application of machine learning technology to the process of identifying the potentially best-in-class hits for the 65 human proteins that interact with SARS-CoV-2 proteins. They then searched for FDA approved substances, perhaps leading to the possibility of inhaled therapeutics, the researchers say. The technology is available through the UC Technology Licensing Office. The graduating scientist has already joined the spinout Sensorygen. However the Ray group has several graduate students pursing lines of research around neurobiology of olfaction, neurodevelopment, computational biology, cheminformatics, electrophysiology, and calcium imaging. Tech Scouts interested in discovering other grant-funded research and emerging innovations in this research ecosystgem should please explore the map in Visible Legacy Navigator, below.
- Caption: University of California, Riverside Ray Lab
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