McMaster University researchers have developed a new generative artificial intelligence (AI) model that can significantly speed up drug discovery, and have already designed an entirely new antibiotic in early tests.
This discovery demonstrates how AI can dramatically improve the time-consuming and costly search for new antibiotics as bacteria and other microorganisms continue to evolve resistance to the current array of drugs.
The new model, called SyntheMol-RL, is trained to explore a vast chemical space of up to 46 billion possible compounds. This is far beyond what can be realistically tested in a laboratory, where even large screens reach about 1 million molecules. Utilizing a set of approximately 150,000 molecular “building blocks” and 50 chemical synthesis reactions, the AI model is designed to generate structurally novel antibiotic candidates.
“In the lab, we can build compounds using a series of small chemical fragments that can be glued together like molecular Lego blocks,” says John Stokes, assistant professor in the lab that developed the new model. “SyntheMol-RL assembles these fragments in different ways and faster than humans ever could to create new, larger compounds. should – Based on that knowledge – it has antibacterial properties. ”
Stokes, a member of the Michael G. DeGroot Institute for Infectious Diseases, said that although generated AI is becoming increasingly useful in the design of novel antibiotic candidates, the key properties that determine the clinical viability of potential drugs remain difficult to assess without extensive and expensive clinical testing.
“Even if a new chemical is discovered in the lab with antibacterial properties, it’s useless if it doesn’t dissolve in the body, is toxic to human cells, or can’t be metabolized and excreted after it’s done its job,” he explains. “Bleach is antibacterial, and so is flame, but they clearly don’t check the other boxes. Good drug candidates have to meet several different criteria, otherwise they’ll never become actual drugs.”
Previous iterations of SyntheMol did not consider these other important properties and only designed molecules with antimicrobial activity. But over the past two years, Stokes’ team has worked with collaborators at Stanford University to refine the model so that it only produces antimicrobial compounds that are easy to develop in the lab and likely to dissolve in the body.
“There are a lot of contradictions between antimicrobial compounds and water-soluble compounds,” says Gary Liu, a graduate student in Stokes’ lab and lead developer of the new model. “Previous research has shown that by filtering out antibacterial compounds, and soluble rear Because our prompts often resulted in significantly fewer viable drug candidates, we incorporated solubility into the generation process and now our model allows us to efficiently design more clinically promising antibiotic candidates. ”
In a new study published on April 23rd and featured on the cover of the magazine’s June issue, molecular systems biologyStokes’ team tested a reinforcement model. They were tasked with producing water-soluble antibiotics that could treat infections caused by bacteria. Staphylococcus aureus Colloquially known as “staph infection,” it quickly became a hit with some.
Among a batch of 79 antibiotics proposed as models, Stokes’ group found one compound of particular interest. It is a novel water-soluble compound that appears to have antibiotic activity against antibiotics. Staphylococcus aureus.
The new computer-designed drug candidate, called Synthecin, was formulated as a topical cream in the lab and tested on drug-resistant wound infections in mouse models.
“Synthecin was very effective at controlling the infection,” says Dennis Kataktan, a graduate student in Stokes’ lab who led the wet lab portion of the study. “It works very well as a topical drug and also shows early promise as something that could be applied or optimized for systemic use in the future.”
New study highlights synth’s promise, but researchers still don’t know how The drug inhibits bacteria, a key step in determining its safety profile and, in turn, its potential to someday enter the clinic, Stokes said. His group is currently actively researching these important “mechanisms of action.”
But regardless of how these studies pan out, the research group sees Synthesin’s discovery as proof that their AI models can rapidly generate high-potential drug candidates, shifting the burden of drug discovery from finding viable compounds to designing and optimizing them.
Stokes says this change is important not just for antibiotic discovery, but for all areas of biochemistry.
“We used models to design new antibiotics, but much more is possible,” says Stokes, a faculty member in the Marnix E. Hersink School of Biomedical Innovation and Entrepreneurship and an executive member of NexusHealth. “We built it to be disease agnostic, which means we can easily generate new drug candidates for diabetes, cancer, and other indications.”
Stokes’ lab continues to enhance SyntheMol, and a more robust version will be available later this year.
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DOI: 10.1038/s44320-026-00206-9
