UTEP and UNM Collaborate on Online Platform to Accelerate COVID-19 Drug Discovery
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.”
Sirimulla supervised a team of UTEP student researchers who built the platform with UNM researchers including Tudor I. Oprea, UNM professor of medicine and chief of the Translational Informatics Division in the Department of Internal Medicine, who jointly supervised the study.
Their findings were released in a paper published this week in Nature Machine Intelligence.
REDIAL-2020 is based on data from the National Center for Advancing Translational Sciences (NCATS) COVID-19 drug repurposing studies.
Researchers applied NCATS data to create a predictive model platform using machine learning algorithms capable of rapidly processing huge amounts of data and teasing out hidden patterns that might not be perceivable by a human researcher. The machine learning models developed in this study were built on Frontera and Stampede2 supercomputing clusters operated by Texas Advanced Computing Center (TACC) at The University of Texas at Austin.
The platform includes 11 models that span a wide spectrum of the SARS-CoV-2 life cycle, including both viral and human (host) targets.
Govinda KC, Ph.D., a graduate from UTEP’s computational science Ph.D. program in December 2020 and the paper’s first author, said the platform will help the research community in reducing the number of molecules used in anti-SARS-CoV-2 experiments, which may speed up the discovery of new drug candidates for COVID-19 treatment.
“With the ongoing SARS-CoV-2 pandemic, new methods for rapidly and systematically screening large compound libraries for new drug candidates are urgently needed,” said KC, an operational research analyst with the U.S. Army Test and Evaluation Command (ATEC).
Currently, REDIAL-2020 can only be used for COVID-19 drug discovery. However, Sirimulla said the platform has the potential to be used to find drug treatments for future pandemics.