Tuberculosis drug discovery gets smarter with AI

When researchers screen potential tuberculosis drugs, they often end up with too many options. Some look promising but later prove to be costly dead ends. "We might get thousands of compounds from a screen and then have to decide which one are we going to work on?" said James Sacchettini, Ph.D., the Rodger J. Wolfe-Welch Foundation Chair in Science, Texas A&M AgriLife Research scientist and professor in the Texas A&M College of Agriculture and Life Sciences Department of Biochemistry and Biophysics and College of Arts and Sciences Department of Chemistry.