University of Texas at Arlington computer science professor, Junzhou Huang, has secured $3.1 million in federal funding to tackle one of medicine’s most time-consuming challenges: figuring out which antibodies will successfully bind to disease-causing targets. In non-science speak, this could increase the speed and decrease the cost of developing new drugs.
“This project is about using AI to automate and improve the early stages of drug development, particularly antibody design,” Huang said. “If we can predict the right binding interactions computationally, it could dramatically speed up the pipeline and lower the risks and costs of drug development.”
Huang’s team recently ranked first in protein contact prediction and placed sixth overall in an international AI challenge competing against Google DeepMind, the University of Washington, and others. For details, head to Dallas Innovates.
