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.
