A team of researchers from Southwest Research Institute (SwRI), The University of Texas at San Antonio (UTSA), and Texas Biomedical Research Institute (Texas Biomed) have developed a machine learning algorithm that identified over two dozen potential treatments for Nipah and Hendra viruses, zoonotic pathogens that can jump from animals to humans. Using SwRI’s Rhodium™ software, the team mapped the protein structure of a related virus, measles, and virtually screened 40 million compounds to identify promising antiviral candidates.
The research, funded by the Department of Defense, leverages machine learning to overcome the challenges of studying these highly pathogenic viruses in high-containment labs. By virtually screening compounds, researchers can quickly prioritize potential treatments, saving significant time and resources. The approach could potentially lead to broad-spectrum therapies for related viruses, including measles, a significant benefit given the high mortality rates associated with Nipah and Hendra infections (40-75% fatality). The findings were presented at the Hendra@30 Henipavirus International Conference.
This innovative approach underscores the power of AI in accelerating drug discovery for challenging infectious diseases, potentially paving the way for faster development of effective treatments for Nipah and Hendra, and related illnesses.
