“We’re doing something a little bit weird,” says Jon Stokes.

The Stokes Lab, under the leadership of graduate student Autumn Arnold, has developed “the ESKAPE Model,” a new AI tool purpose-built to identify new antibiotics.

What’s weird is not that their new model can identify novel drug candidates in the blink of an eye; that’s its job.

What’s weird is that they are releasing it to the public immediately — and for free.

Standard protocol is to write a research paper about the development of the tool and to submit it to academic journals, says Stokes, an assistant professor in the department of Biochemistry and Biomedical Sciences, in the Faculty of Health Sciences.

If journal editors accept the paper, the research — and the tool — would undergo rigorous peer review by other scientists. This process can help with refinement, provide the research team with suggestions, and flag potential issues.

But that can all take a year or more, and Stokes doesn’t want to wait.

“We’re still working on the paper that describes this work, but we wanted to get the tool out there in the meantime,” he says.

“We know it’s not perfect, but even in its imperfect form it can have immediate utility for anyone doing antibiotic discovery research.”

Stokes and Arnold are crowdsourcing the peer-review process, putting their new tool in the hands of as many researchers as possible so they can get direct feedback from the people who actually use it.

This way, they can continuously refine the model to reflect the long-term, evolving demands of the field.

The decision to publish the tool like this initially kept the researchers up at night, Stokes admits.

“Perhaps it’s to the detriment of my academic career, but there are more important things than publications and citation metrics,” he says.

“Sitting on this until it’s perfect just didn’t feel right. We hope that getting it out there now will enable us to more rapidly receive user feedback and accelerate others’ ability to discover important new drugs.”

The new tool screens for chemicals that may have therapeutic potential against ESKAPE pathogens, a globally recognized list of the world’s most dangerous and drug-resistant bacteria.

“Due to their ability to resist most antibiotics, there are very few options for treating the infections caused by the ESKAPE pathogens,” Arnold says.

“Using machine learning, our tool is allowing researchers from all over the world to rapidly assess their own chemicals for new drug candidates that target these bacteria.”

The tool is accessible in more ways than one — not only freely available, but also extremely easy to use, says Arnold, who developed it in collaboration with researchers in McMaster professor Andrew McArthur’s lab.

“We designed the model so that even people who have no experience with AI can use it with ease,” Arnold says.

The model prompts users to input SMILES codesv— alphanumeric strings that represent the structure of different chemical compounds. Following a copy-and-paste, users quickly receive AI-guided predictions about whether their chemicals have therapeutic potential against any of the ESKAPE pathogens.

ESKAPE Model users can screen upward of 20,000 chemicals in the average workday, without ever picking up a pipette or spending a dollar, the research team says.

For context, the same output using a traditional wet lab approach would take several weeks and cost thousands of dollars — and may not ever yield any positive results, Arnold says.

“This approach allows researchers to prioritize which molecules to test in the lab,” Arnold says. “It can help save people a lot of time and money.”

Arnold has used the model herself, to screen 12 million molecules — a feat that would take longer than an entire career by traditional means.

Her work has led to the discovery of multiple new antibiotic candidates, which are now under study in the Stokes Lab.

Stokes, part of McMaster’s Global Nexus and the Michael G. DeGroote Institute for Infectious Disease Research, compares the ESKAPE Model to the Comprehensive Antibiotic Resistance Database (CARD), another publicly accessible, McMaster-made tool designed to assist the global research community in the fight against drug-resistant bacteria.

CARD catalogues genomic data pertaining to drug resistance. Paired with the ESKAPE Model’s ability to identify new antibiotics to overcome resistance, Stokes says the tools are cementing McMaster’s spot at the cutting edge of antimicrobial research.

“McMaster is quickly becoming a one-stop shop for everything antibiotics,” he says.

The ESKAPE Model officially launched Jan. 27.