AI menaces superbug by identifying potent antibiotic
Neural networks have helped scientists to develop an antibiotic capable of fighting a highly resistant superbug commonly found in hospitals.
"Acinetobacter can survive on hospital doorknobs and equipment for a week or longer, and can take up antibiotic resistance genes from its environment," said Jonathan Stokes, an assistant professor of biochemistry and biomedical sciences at McMaster University. "It's really common now to find A. baumannii isolates that are resistant to nearly every antibiotic."
Stokes and his colleagues at McMaster University and MIT turned to AI to identify compounds that can combat the microbe. First, they exposed 7,500 different molecules to a strain of the bacteria grown in a lab dish to see whether they 1would inhibit its growth. They used that dataset to train a machine learning classifier to learn what chemical features in compounds gave the bacteria grief.
The model was then used to analyze a new dataset comprising 6,680 compounds it had not seen before, to predict whether they might make promising antibiotics.
