Queensland University of Technology Professor Luis Pedro Coelho has used machine learning to explore the global microbiome to find almost a million antimicrobial peptides that could be used as antibiotics. (Anthony Weate / QUT via SWNS) By Stephen Beech via SWNS Hundreds of thousands of potential sources of new antibiotics to fight deadly drug-resistant superbugs have been found in the natural world using artificial intelligence . An international research team utilized machine learning to identify 863,498 "promising" antimicrobial peptides – small molecules that can kill or inhibit the growth of infectious microbes.
The findings of the study, published in the journal Cell , come amid a renewed focus on combatting antimicrobial resistance (AMR) as humanity contends with soaring numbers of "superbugs" resistant to current drugs. Computational biologist Professor Luis Pedro Coelho, of the Queensland University of Technology (QUT), Australia , said: “There is an urgent need for new methods for antibiotic discovery. “It is one of the top public health threats, killing 1.
27 million people each year.” Without intervention, it is estimated that AMR could cause up to 10 million deaths every year by 2050. Coelho, a researcher at the QUT Centre for Microbiome Research which studies the structure and function of microbial communities, said: “Using artificial intelligence to understand and harness the power of the global microbiome will hopefully drive innovative research for b.
