In a groundbreaking advance against the escalating crisis of antibiotic resistance, researchers at MIT have harnessed artificial intelligence to design entirely new antibiotics capable of tackling two notorious drug-resistant bacteria.
The antibiotics can be used to treat Neisseria gonorrhoeae, the culprit behind gonorrhea, and methicillin-resistant Staphylococcus aureus (MRSA), a common cause of severe skin and bloodstream infections.
The study, published today in the journal Cell, comes at a critical time. Over the past 45 years, the FDA has approved only a handful of new antibiotics, most of which are mere tweaks on existing drugs.
Meanwhile, bacterial resistance has surged, contributing to nearly 5 million deaths annually worldwide from drug-resistant infections.
Traditional drug discovery methods, reliant on screening known chemical libraries, have struggled to keep pace.
But MIT’s Antibiotics-AI Project is flipping the script by using generative AI to explore uncharted “chemical spaces”, vast realms of hypothetical molecules that don’t exist in nature or labs yet.
Led by James Collins, the Termeer Professor of Medical Engineering and Science at MIT’s Institute for Medical Engineering and Science, the team generated over 36 million potential compounds computationally.
These were then screened using machine-learning models trained to predict antibacterial activity, toxicity to human cells, and novelty.
The result? Antibiotics that are structurally unlike any on the market, operating through fresh mechanisms that rupture bacterial cell membranes, making it harder for resistance to evolve.
“We’re excited about the new possibilities that this project opens up for antibiotics development,” Collins said in a statement.
The researchers employed two innovative strategies. For N. gonorrhoeae, they adopted a “fragment-based” approach.
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