LAUSR

cillin and forever changed the face of medicine, Massachusetts Institute of Technology (MIT) researchers made another discovery potentially as valuable as Fleming’s ground-breaking work. They used artificial intelligence (AI) algorithms based on machine learning to trawl through a vast digital collection of pharmaceutical compounds to identify novel antibacterial molecules [1], and identified a new antibiotic that they dubbed halicin (named after 2001’s HAL 9000). Halicin, it turns out, acts in a manner different from conventional antimicrobials—by damaging the ability of bacteria to maintain an electrochemical gradient necessary for survival. In the MIT study, a deep neural network model was developed to screen millions of molecules in mere days to identify potential antibiotics that employ mechanisms different from those of existing drugs. After screening numerous chemical libraries, the team identified halicin from the Drug Repurposing Hub [2], as a compound that shows bactericidal action against a wide assortment of pathogens, including Mycobacterium tuberculosis, carbapenem-resistant Enterobacteriaceae, Clostridioides difficile, and Acinetobacter baumannii. This seminal study underscores the value of AI for expanding our pharmaceutical arsenal to include structurally distinct antibacterial drugs. Another potential role for AI in the realm of infectious diseases is to provide prescription guidance for antibiotics. The use of such decision support systems would be expected to reduce inappropriate prescriptions of antibiotics and antimicrobial resistance. Such systems would apply machine learning techniques or expert systems methods (Figure 1) to data from healthcare databases. Predictions based on a patient’s medical information would estimate the probability of the Artificial Intelligence: The New Alexander Fleming