College StationA single screen can yield thousands of drug candidates.
Texas A&M researchers are training machine learning to flag which tuberculosis compounds are worth pursuing, cutting costly dead ends in a disease concentrated across South Asia and Africa.
Chemist James Sacchettini’s team is refining which screening signals the models should trust most.
Sources: Phys.org