How A.I. helps sniff out new antibiotics

February 25, 2020, 3:08 PM UTC

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Happy Tuesday, readers.

We’ve known for a while now that there’s a serious dearth of new antibiotics—a frightening prospect given the increasing number of Americans being sickened by so-called “superbugs” that can ward off the most powerful available treatments.

But what if artificial intelligence and deep learning can help identify the most promising new antibiotic candidates?

That’s what a team of researchers at MIT is testing out—and they report in a recent paper that their algorithm was successfully able to identify a new antibiotic that they’ve named halicin (a cheeky reference to the infamous computer in Stanley Kubrick’s 2001: A Space Odyssey).

Regina Barzilay, a professor of computer science at MIT and one of the study’s senior authors, walked me through how, exactly such a process works.

“Think about how you’re using Amazon,” she said. “They know what you bought and what you didn’t, so they’re able to give you a more limited but curated list of recommendations.”

Similarly, chemists can train a machine learning algorithm to look for specific characteristics in a new compound.

“A chemist knows there’s a certain combination of atoms more likely to kill a bug because that’s what their experience has been,” explained Barzilay. “Machines can try to identify the combination between the molecular structure and how a combination of things can kill certain bacteria.”

Much more on our conversation here.

Read on for the day’s news.

Sy Mukherjee


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