AI vs. bacteria
MIT researchers have used a new type of neural network model to identify a powerful antibiotic compound that kills many of the world’s most problematic disease-causing bacteria, including some that are resistant to all previously known antibiotics.
The algorithm, which can screen more than 100 million chemical compounds in a matter of days, is designed to pick out potential antibiotics that use different mechanisms from existing drugs. Very few new antibiotics have been introduced over the past few decades.
“We wanted to develop a platform that would allow us to harness the power of artificial intelligence to usher in a new age of antibiotic drug discovery,” says James Collins, a professor of biological engineering and a member of MIT’s Institute for Medical Engineering and Science.
Collins and Regina Barzilay, a professor of electrical engineering and computer science in MIT’s Computer Science and Artificial Intelligence Laboratory, are the senior authors of the study and the faculty co-leads for MIT’s Abdul Latif Jameel Clinic for Machine Learning in Health (J-Clinic). Jonathan Stokes, a postdoc at MIT and the Broad Institute of MIT and Harvard, is the lead author of the study, which appeared in Cell.
The MIT team used the model to look for chemical features that make molecules effective at killing E. coli. From a library of about 6,000 potential drug compounds, it picked out one that was predicted to have strong antibacterial activity and whose chemical structure was different from that of any existing antibiotic. This molecule, which the researchers decided to call halicin (after the fictional artificial intelligence system from 2001: A Space Odyssey), had been previously investigated as a possible diabetes drug.
The researchers tested halicin against dozens of bacterial strains isolated from patients and grown in lab dishes. It was able to kill many that are resistant to treatment, including Clostridium difficile, Acinetobacter baumannii, and Mycobacterium tuberculosis.
In studies of mice, halicin also cleared infections by a strain of A. baumannii that is resistant to all known antibiotics; it worked within 24 hours. The researchers plan to pursue further studies of halicin, working with a pharmaceutical company or nonprofit organization, in hopes of developing it for use in humans. In a larger screen of about 100 million molecules that took just three days, the researchers also identified 23 other promising antibiotic candidates, which they plan to test further.
Geoffrey Hinton tells us why he’s now scared of the tech he helped build
“I have suddenly switched my views on whether these things are going to be more intelligent than us.”
Meet the people who use Notion to plan their whole lives
The workplace tool’s appeal extends far beyond organizing work projects. Many users find it’s just as useful for managing their free time.
Learning to code isn’t enough
Historically, learn-to-code efforts have provided opportunities for the few, but new efforts are aiming to be inclusive.
Deep learning pioneer Geoffrey Hinton has quit Google
Hinton will be speaking at EmTech Digital on Wednesday.
Get the latest updates from
MIT Technology Review
Discover special offers, top stories, upcoming events, and more.