A new study used machine learning to predict potential new antibiotics in the global microbiome, which study authors say marks a significant advance in the use of artificial intelligence in antibiotic resistance research. The report, published Wednesday in the journal Cell, details the findings of scientists who used an algorithm to mine the “entirety of the microbial diversity that we have on earth – or a huge representation of that – and find almost 1m new molecules encoded or hidden within all that microbial dark matter”, said César de la Fuente , an author of the study and professor at the University of Pennsylvania. De la Fuente directs the Machine Biology Group , which aims to use computers to accelerate discoveries in biology and medicine.
Without such an algorithm, De la Fuente said, scientists would have had to use traditional methods like collecting water and soil to find molecules within those samples. That can be challenging because microbes are everywhere – from the ocean to the human gut. “It would have taken many, many, many, many years to do that, but with an algorithm, we can sort through vast amounts of information, and it just speeds up the process,” De la Fuente said.
Scientists use AI to discover new antibiotic to treat deadly superbug Read more The research is urgent to public health, the author said, because antimicrobial resistance caused more than 1.2 million deaths in 2019 . That number could increase to 10 million deaths annually by .
