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Projected Impact of Resistance
The estimated rise in deaths due to drug-resistant infections.
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AI Model Designs New Antibiotic for Resistant Bacteria
AI Model Explores 46 Billion Compounds to Combat Antimicrobial Resistance McMaster University researchers have developed a generative AI model called SyntheMol-RL that can explore a vast chemical space of up to 46 billion possible compounds to design new antibiotics11. The AI model represents a significant advance in rapid drug discovery, addressing the urgent need for new antimicrobial medicines as resistant bacteria continue to evolve. Drawing on roughly 150,000 molecular building blocks and a set of 50 chemical synthesis reactions, the AI model generates structurally novel antibiotic candidates far beyond what could realistically be tested in traditional laboratory settings, where even large-scale screens top out at around a million molecules22. Source: News-Medical Assistant Professor Jon Stokes, whose laboratory developed the new model at the Michael G. DeGroote Institute for Infectious Disease Research, explains that SyntheMol-RL configures molecular fragments "like molecular Lego blocks" in different ways, faster than humans ever could, to create new, larger chemical compounds that should be antibacterial based on its knowledge11. This approach marks a fundamental shift in how drug discovery operates, moving from searching for viable compounds to actively designing and optimizing them. Generative AI Model Addresses Critical Clinical Viability Challenges While generative AI has shown increasing effectiveness at designing novel antibiotic candidates, key properties that determine clinical viability have remained difficult to assess without extensive and expensive laboratory testing. Past iterations of SyntheMol exclusively designed molecules with antibacterial activity without consideration for other critical properties like solubility, toxicity, and metabolization. "It doesn't matter if you find a new chemical that's antibacterial in the lab if it can't dissolve inside the body, if it's toxic to human cells, or if it can't be metabolized and expelled after it has done its job," Stokes explains22. Over the past two years, Stokes' team collaborated with Stanford University to refine the model so it only generates antibacterial compounds that are easy to develop in the lab and likely to be soluble in the body. Gary Liu, a graduate student in Stokes' lab and lead developer of the new model, notes that there is significant conflict between compounds that are antibacterial and compounds that are water soluble. By building solubility directly into the gener...
Can AI Help Tackle Antibiotic Overuse in Kids?
Harvard Business School Assistant Professor Michael Lingzhi Li has spent his career helping public health officials respond to such crises as the Ebola outbreak of the 2010s and the COVID-19 pandemic. But he’s not a doctor; he’s a researcher who focuses on how to translate the latest technical advances into real-world solutions in health care. Now, in partnership with Boston Children’s Hospital (BCH), he has turned his attention to antibiotic resistance and the challenges of bridging the gap between theoretical solutions and patient impact.Creating an effective algorithm is a multistep process, Li says, starting with identifying a real pain point—“not just a problem that feels like a pain point from an academic point of view”—and understanding where a potential solution would fit in the clinical workflow. It was during a conversation with a doctor at BCH, where Li is now the codirector of the Computational Healthcare Analytics Program, that he learned that the standard of care for treating children with recurring urinary tract infections was prophylactic antibiotics. The approach worked for many but not all, and Li envisioned an AI tool that could reduce overprescribing by helping doctors determine which patients would benefit from the treatment.In 2018, Li and his collaborators began to build an algorithm that would predict the effect of the antibiotic treatment and recommend an action to take. In the process, they realized that in addition to building an accurate algorithm, they had to address two challenges: how to rigorously evaluate the system in a way that would convince physicians of its effectiveness without relying on a randomized controlled trial, and how to ensure interpretability so that clinicians could understand and trust the algorithm’s recommendations and apply them in practice. These challenges have since shaped distinct research directions that Li has worked on extensively.The question is, when we put it in the hands of the clinicians, do they choose to use it, and if so, does the tool help patients? That’s the true test.The final challenge was implementation. That’s where Li and his BCH collaborators are now. “Our algorithm works in that it can fairly accurately predict if antibiotics would be useful to a particular patient,” Li says. “The question is, when we put it in the hands of the clinicians, do they choose to use it, and if so, does the tool help patients? That’s the true test.”Implementing the AI tool in 12 clinics associated wi...
McMaster AI model designs new antibiotic candidate in early tests
Researchers at McMaster University said their SyntheMol-RL AI model designed a new antibiotic candidate called synthecin. In mouse wound infection models, th...
5 Best Evidence-Based AI Tools for Antimicrobial Stewardship in ...
Why Evidence-Based AI Matters for Antimicrobial Stewardship Academic hospitals face intense pressure to make rapid, defensible antibiotic choices between patients. Unverified AI output increases the risk of inconsistent recommendations and clinician liability.
