Using AI to Fight Superbugs: Scientists Show Intelligent Machines Can Take On Drug-Resistant Bacteria

Saturday - December 23 2023, 06:53 UTC - 11 months ago

MIT researchers have built and tested an AI system to identify whole new classes of antibiotics and predict which are likely safe for people. The AI sifted over 12 million compounds and found a promising candidate that was effective in mice against MRSA, an antibiotic-resistant strain of bacteria. This study is evidence that AI can be used to speed up the process of drug discovery.

Antibiotics have saved countless lives and are a crucial tool in modern medicine. But we’re losing ground in our battle against bacteria. In the middle of the last century, scientists discovered whole new classes of antibiotics. Since then, the pace of discovery has slowed to a trickle, and the prevalence of antibiotic-resistant bacteria has grown.

There are likely antibiotics yet to be discovered, but the chemical universe is too big for anyone to search. In recent years, scientists have turned to AI. Machine learning algorithms can whittle enormous numbers of potential chemical configurations down to a handful of promising candidates for testing.

To date, scientists have used AI to find single compounds with antibiotic properties. But in a new study, published yesterday in Nature, MIT researchers say they’ve built and tested a system that can identify whole new classes of antibiotics and predict which are likely safe for people.

The AI sifted over 12 million compounds and found an undiscovered class of antibiotics that proved effective in mice against methicillin-resistant Staphylococcus aureus (MRSA), a deadly strain of drug-resistant bug.

While these AI-discovered antibiotics still need to prove themselves safe and effective in humans by passing the standard gauntlet of clinical testing, the team believes their work can speed discovery on the front end and, hopefully, increase our overall hit rate.

Exploring Drug Space .

Scientists are increasingly using AI sidekicks to speed up the process of discovery. Most famous, perhaps, is DeepMind’s AlphaFold, a machine learning program that can model the shapes of proteins, our body’s basic building blocks. The idea is that AlphaFold and its descendants can speed up the arduous process of drug research. So strong is their conviction, DeepMind spun out a subsidiary in 2021, Isomorphic Labs, dedicated to doing just that.

Other AI approaches have also shown promise. An MIT group, in particular, has been focused on developing entirely new antibiotics to fight superbugs. Their first study, published in 2020, established the approach could work, when they found halicin, a previously undiscovered antibiotic that could readily take out drug-resistant E. coli.

In a followup earlier this year, the team took aim at Acinetobacter baumannii, “public enemy No. 1 for multidrug-resistant bacterial infections,” according to McMaster University’s Jonathan Stokes, a senior author on the study.

"Acinetobacter can survive on hospital doorknobs and equipment for long periods of time, and it can take up antibiotic resistance genes from its environment. It’s really common now to find A. baumannii isolates that are resistant to nearly every antibiotic," Stokes said at the time.

After combing through 6,680 compounds in just two hours, the AI highlighted a few hundred promising candidates. The team tested 240 of these that were structurally different from existing antibiotics. They surfaced nine promising candidates, including one, abaucin, that was quite effective against A. baumannii.

Both studies showed the approach could work, but only yieded single molecules; abaucin clocked in at about 20 atoms, and halicin — the more modern of the two — had 374 atoms.