AI Drug Design: AI Models Craft Effective Antibiotics

Some today fear that artificial intelligence will one day destroy humanity. But if the rise of the machines doesn’t get us, drug-resistant bacteria just might. These microscopic killers already claim millions of lives each year worldwide, and the world’s arsenal of effective antibiotics is dwindling.

But could one threat be trained perhaps to help stave off the other? A study published today in the journal Cell certainly suggests the possibility. A team led by Jim Collins, MIT professor of biological engineering, showed how generative AI algorithms trained on vast datasets of antibacterial substances could dream up millions of previously unimagined molecules with predicted microbe-killing power—some of which proved potent in mouse experiments.

The researchers synthesized a small subset of these AI-designed molecules and found them lethal to superbugs responsible for drug-resistant gonorrhea and stubborn staphylococcus skin infections.

“It’s a great addition to this emerging field of using AI for antibiotic discovery,” says César de la Fuente, a synthetic biologist at the University of Pennsylvania who was not involved in the research. “It shows quite well how generative AI can produce molecules with real-world activity,” he adds. “It’s elegant and potentially clinically meaningful.”

A social-enterprise non-profit created by Collins, called Phare Bio, now plans to advance these and other AI-discovered antibiotics toward clinical development.

The candidate antibiotics build on earlier finds from Collins’ lab—including halicin, a potent broad-spectrum antibiotic identified in 2020; a more targeted agent called abaucin with activity against Acinetobacter baumannii, a major cause of hospital-acquired infections; and a novel structural class of molecules described last year that proved effective against the superbugs MRSA and VRE.

With the team’s earlier discoveries, however, Collins and his colleagues were still mining existing chemical libraries, using deep-learning models to spot overlooked compounds with antibacterial potential. The new work sets down a new path altogether: rather than searching for hidden gems in familiar territory, the generative AI platform starts from scratch, conjuring entirely new molecular structures absent from any database.

“This is moving from using AI as a discovery tool to using AI as a design tool,” Collins says. The shift, he adds, opens new frontiers in antibiotic discovery—unexplored territory that could harbor the next generation of lifesaving drugs.

Anti-Germ Intelligence Proves Its Mettle

To train their generative AI model, Collins and his colleagues first used a neural network framework to virtually screen more than 45 million chemical fragments—the building blocks of would-be drugs—looking for pieces predicted to have activity against Neisseria gonorrhoeae (the cause of sexually transmitted gonorrhea infections) and Staphylococcus aureus (the germ behind deadly bloodstream…