AI-Powered Prediction of Antimicrobial Peptides in Human Serum: A New Strategy Against Resistant Bacteria

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In the 20th century, antibiotics transformed medicine. Infections that once killed millions could be cured with a pill or injection. Surgeries became safer, cancer treatments more effective, and advanced medical interventions, such as organ transplants, became possible, all because doctors could rely on these drugs to control infections. Unfortunately, today, that foundation is crumbling. Bacteria are evolving faster than medicine can keep up. Common antibiotics are failing, and infections that were once easily treatable are becoming deadly again. In 2019 alone, antimicrobial resistance was linked to nearly five million deaths worldwide, making it deadlier than HIV or malaria. The economic cost is equally staggering: the World Bank warns of trillions lost in global productivity and millions pushed into poverty if nothing changes. This crisis, caused by antimicrobial resistance, has been described as a “silent pandemic.” Unlike a sudden outbreak, it spreads quietly, making routine medical care slightly more dangerous each year. Yet amid this grim outlook, new research is opening a window of hope. At the forefront of new innovations in this area are Dr. Kai Hilpert of City St George's, University of London, and his colleagues, who are pioneering an approach that combines biology, chemistry, and artificial intelligence to reinvent how we discover infection-fighting medicines. Their work has been recognised with a prestigious award from the UK’s Biotechnology and Biological Sciences Research Council, BBSRC.

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