Skip to Content
77 Mass Ave

AI vs. bacteria

A neural network helped identify a new antibiotic that kills some of the world’s most drug-resistant germs.
April 15, 2020
Amrita Marino

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.

Keep Reading

Most Popular

Large language models can do jaw-dropping things. But nobody knows exactly why.

And that's a problem. Figuring it out is one of the biggest scientific puzzles of our time and a crucial step towards controlling more powerful future models.

OpenAI teases an amazing new generative video model called Sora

The firm is sharing Sora with a small group of safety testers but the rest of us will have to wait to learn more.

Google’s Gemini is now in everything. Here’s how you can try it out.

Gmail, Docs, and more will now come with Gemini baked in. But Europeans will have to wait before they can download the app.

This baby with a head camera helped teach an AI how kids learn language

A neural network trained on the experiences of a single young child managed to learn one of the core components of language: how to match words to the objects they represent.

Stay connected

Illustration by Rose Wong

Get the latest updates from
MIT Technology Review

Discover special offers, top stories, upcoming events, and more.

Thank you for submitting your email!

Explore more newsletters

It looks like something went wrong.

We’re having trouble saving your preferences. Try refreshing this page and updating them one more time. If you continue to get this message, reach out to us at customer-service@technologyreview.com with a list of newsletters you’d like to receive.