The MIT team first used the deep learning model to screen a library of 6,000 molecules for those which may be effective against E. coli. The search detected halicin, which the authors tested against a number of cultured bacterial strains, discovering the molecule "displays bactericidal activity against a wide phylogenetic spectrum of pathogens including Mycobacterium tuberculosis and carbapenem-resistant Enterobacteriaceae," the authors wrote. The researchers also found halicin kills C. difficile and a "pan-resistant" infection in mouse models. In a subsequent screen of more than 107 million molecules from the ZINC15 database provided by the University of California, San Francisco, the AI tool identified eight molecules with structures distinct from known antibiotics but which might have potent anti-bacterial properties. Professor Jacob Durrant of the University of Pittsburgh, a drug design researcher who was not part of the study, told The Guardian: "The work really is remarkable.