Professor James Zou and others described their model and experimental validation of these new compounds in 'Generative AI for designing and validating easily synthesizable and structurally novel antibiotics,' published March 22 in the journal Nature Machine Intelligence.

March 28, 2024 - By Rachel Tompa 

With nearly 5 million deaths linked to antibiotic resistance globally every year, new ways to combat resistant bacterial strains are urgently needed.

Researchers at Stanford Medicine and McMaster University are tackling this problem with generative artificial intelligence. A new model, dubbed SyntheMol (for synthesizing molecules), created structures and chemical recipes for six novel drugs aimed at killing resistant strains of Acinetobacter baumannii, one of the leading pathogens responsible for antibacterial resistance-related deaths.

"There’s a huge public health need to develop new antibiotics quickly," said James Zou, PhD, an associate professor of biomedical data science and co-senior author on the study. "Our hypothesis was that there are a lot of potential molecules out there that could be effective drugs, but we haven’t made or tested them yet. That’s why we wanted to use AI to design entirely new molecules that have never been seen in nature."

Before the advent of generative AI, the same type of artificial intelligence technology that underlies large language models like ChatGPT, researchers had taken different computational approaches to antibiotic development. They used algorithms to scroll through existing drug libraries, identifying those compounds most likely to act against a given pathogen. This technique, which sifted through 100 million known compounds, yielded results but just scratched the surface in finding all the chemical compounds that could have antibacterial properties.

Read the full article.

Excerpted from, 'Generative AI develops potential new drugs for antibiotic-resistant bacteria,' Stanford Medicine News Center.