The researchers said that the worldwide spread of drug-resistant bacteria has created an urgent need for new antibiotics, but even modern AI methods are limited at isolating promising chemical compounds, particularly when researchers must also find ways to manufacture and test these new AI-guided drugs.
This new generative AI model, named SyntheMol, can design new antibiotics to counter this problem – in particular, to stop the spread of Acinetobacter baumannii, which the World Health Organization (WHO) has identified as one of the world’s most dangerous antibiotic-resistant bacteria.
The researchers said that A. baumannii is ‘notoriously difficult to eradicate’ and can cause pneumonia, meningitis and infect wounds. They added that few treatment options remain.
In a statement, lead author and an assistant professor in McMaster’s Department of Biomedicine & Biochemistry, Jonathan Stokes, said: “Antibiotics are a unique medicine. As soon as we begin to employ them in the clinic, we're starting a timer before the drugs become ineffective, because bacteria evolve quickly to resist them.
“We need a robust pipeline of antibiotics and we need to discover them quickly and inexpensively. That's where the artificial intelligence plays a crucial role.”
According to the research team, SyntheMol can access ‘tens of billions’ of promising molecules quickly and cheaply.
The researchers drew from a library of 132,000 molecular fragments, which they said fit together like Lego pieces but are all very different in nature, and then cross-referenced these molecular fragments with a set of 13 chemical reactions.
This enables the researchers to identify 30 billion two-way combinations of fragments to design new molecules with the ‘most promising’ antibacterial properties.
Each of the molecules designed by this model was in turn fed through another AI model trained to predict toxicity. The process yielded six molecules which display potent antibacterial activity against A. baumannii and are also non-toxic.
"Synthemol not only designs novel molecules that are promising drug candidates, but it also generates the recipe for how to make each new molecule. Generating such recipes is a new approach and a game changer because chemists do not know how to make AI-designed molecules,” said co-author James Zou, an associate professor of biomedical data science at Stanford University.
The research, funded in part by the Weston Family Foundation, the Canadian Institutes of Health Research, and Marnix and Mary Heersink, was published in Nature Machine Intelligence and can be accessed in full here.
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