Skip to Content
MIT News: 77 Mass Ave

Large language models may speed drug discovery

An AI technique developed by MIT researchers can screen more than 100,000 compounds in a day.

August 22, 2023
bottle of pills spilled out onto a plain blue surface
Towfiqu barbhuiya/unsplash

Computational models have been a major time saver when it comes to predicting which protein molecules could make effective drugs, but many of those methods themselves take a lot of time and computing power. 

Now researchers at MIT and Tufts have devised an alternative approach based on an algorithm known as a large language model, which can figure out which words (or, in this case, amino acids) are most likely to appear together. The model can match target proteins and potential drug molecules without the computationally intensive step of calculating each protein’s 3D structure from its amino acid sequence. The resulting system can screen more than 100 million drug-protein pairs in a single day.

The researchers tested their model by screening a library of about 4,700 candidate drug molecules for their ability to bind to a set of 51 enzymes. From the top hits, they tested 19 drug-protein pairs; the tests revealed that 12 had strong binding affinity, whereas nearly all of the many other possible pairs would have no affinity. 

“Part of the reason why drug discovery is so expensive is because it has high failure rates,” says Rohit Singh, PhD ’12, a CSAIL research scientist and one of the lead authors of a paper on the work. “If we can reduce those failure rates by saying up front that this drug is not likely to work out, that could go a long way in lowering the cost of drug discovery.”

Keep Reading

Most Popular

It’s time to retire the term “user”

The proliferation of AI means we need a new word.

The problem with plug-in hybrids? Their drivers.

Plug-in hybrids are often sold as a transition to EVs, but new data from Europe shows we’re still underestimating the emissions they produce.

Sam Altman says helpful agents are poised to become AI’s killer function

Open AI’s CEO says we won’t need new hardware or lots more training data to get there.

An AI startup made a hyperrealistic deepfake of me that’s so good it’s scary

Synthesia's new technology is impressive but raises big questions about a world where we increasingly can’t tell what’s real.

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.