Data scientists at the Icahn School of Medicine at Mount Sinai in New York and colleagues have created an artificial intelligence model that may more accurately predict which existing medicines, not currently classified as harmful, may in fact lead to congenital disabilities. The model, or “knowledge graph,” described in the July 17 issue of the Nature journal Communications Medicine, also has the potential to predict the involvement of pre-clinical compounds that may harm the developing fetus. The study is the first known of its kind to use knowledge graphs to integrate various data types to investigate the causes of congenital disabilities.
EL PASO, Texas –Drug discovery researchers at The University of Texas at El Paso and the University of New Mexico have leveraged their expertise to develop a rapid online tool to accelerate the discovery of drug therapies for SARS-CoV-2, the virus that causes COVID-19.
REDIAL-2020 is an open-source online suite of computational models that will help scientists rapidly screen small molecules for their potential COVID-19-fighting properties. The platform is available as a web application through DrugCentral.org/Redial.
“REDIAL-2020 is a machine learning platform we developed to estimate the activities of drugs for anit-SARS-COV-2 activities,” said Suman Sirimulla, Ph.D., assistant professor of pharmaceutical sciences at UTEP’s School of Pharmacy. “The platform allows scientists from around the world to identify small molecules that can inhibit SARS-CoV-2, in order to develop new drugs or repurpose existing drugs to treat COVID-19.”