Cambridge, Mass. Researchers at MIT and McMaster University, based in Ontario, Canada, have found a new antibiotic treatment that can kill a common bacteria in hospital infections thanks to machine learning. The study, published May 25 in Nature Chemical Biology, used machine learning to determine which chemicals could inhibit the growth of Acinetobacter baumannii, a common hospital bacterium. After analyzing 6,680 compounds in two hours, the algorithm identified several hundred options. The researchers identified nine antibiotics. A compound originally discovered as a potential diabetes drug was effective at killing A. baumannii, but had no effect on other types of bacteria—a desirable trait. In mouse studies, the researchers showed that the drug, which they call Abaucin, treats wound infections caused by A. baumannii, according to the May 25 MIT news release. In lab tests, it also worked against several drug-resistant strains of A. baumannii isolated from human patients. "This finding could significantly accelerate and expand our search for new antibiotics," said James Collins, professor of medical engineering and science at MIT's Institute of Medical Engineering and Science. He continued, "I'm excited that this study shows that we can use AI to combat problematic pathogens like A. baumannii."