HMN 2026: How to Use AI to tackle global superbug crisis

The development of new antibiotics could be sped up thanks to a new tool that tests the reliability of AI. University of Queensland researchers designed a framework to address the global threat of antimicrobial resistance, testing whether AI can provide reliable reasoning during antibiotic development. Dr. Abdulmujeeb Onawole, from UQ’s Center for Superbug Solutions at the Institute for Molecular Bioscience, said drug-resistant bacteria were one of the greatest threats to global health, and there was an urgent need for new antibiotics.

“AI is revolutionizing drug development, but scientists struggle to trust its recommendations because the technology often can’t explain its reasoning,” Onawole said. “We call this the ‘black box’ of AI—where AI provides an answer but there’s no explanation of how it got there—and this is preventing scientists understanding the chemical reasoning behind its predictions.

“This lack of transparency is dangerous during antibiotic development, as misleading AI explanations can lead to incorrect decisions and wasted resources in the lab.”

Antimicrobial resistance, including resistance to antibiotics, is threatening health care globally by limiting effective treatment options against multidrug-resistant pathogens known as ‘superbugs.’

“This is a high-stakes field, and while AI can help us to save lives faster, we want to ensure the humans involved can make an informed decision,” Onawole said. “Longer term, this could contribute to the faster discovery of new antibiotics to combat drug-resistant superbugs.”

For the study published in the Journal of Cheminformatics, researchers developed three AI models using data sets of chemical compounds previously evaluated against the superbug bacterium Staphylococcus aureus.

The framework was tested on each AI model and examined whether AI could correctly identify important drug structures and interpret “activity cliffs”—scenarios where small chemical changes altered a drug’s effectiveness.

Dr. Johannes Zuegg from UQ’s Center for Superbug Solutions said the research found all three AI models were good at spotting known antibiotic structures but differed significantly in their ability to explain what made a molecule active or inactive in developing antibiotics.

“We have shown our framework can successfully assess whether AI systems can provide trustworthy chemical explanations, which is critical to medical chemists in drug development,” he said. “This is an important step toward speeding up the integration of AI into antibiotic research.”

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