Addressing the global threat of antimicrobial resistance (AMR), researchers from the Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), India, and Inria Saclay, France, have unveiled an advanced artificial intelligence (AI)-powered solution designed to support clinicians in managing drug-resistant bacterial infections. This collaborative innovation leverages the repurposing of existing antibiotics to identify viable treatment alternatives, marking a major step forward in combating AMR through technology.
AMR, a growing global health emergency, arises when bacteria adapt to evade the effects of antibiotics, rendering previously effective treatments powerless. As a result, even routine infections such as urinary tract infections, pneumonia, or infected wounds may escalate into life-threatening conditions. This issue is particularly acute in low- and middle-income nations, where over 70% of hospital-acquired infections show resistance to at least one commonly used antibiotic, according to recent findings.
The traditional development pipeline for new antibiotics is notoriously slow and cost-intensive, often requiring more than a decade and hundreds of millions of dollars for a single drug. In response to this challenge, healthcare systems are increasingly turning to drug repurposing—an approach that seeks new applications for existing medications—as a more efficient alternative.
To support this strategy, a multidisciplinary team led by Dr. Emilie Chouzenoux from Inria Saclay and Dr. Angshul Majumdar from IIIT-Delhi has developed a hybrid machine learning algorithm capable of recommending alternative treatment options for resistant infections. The research team also comprises research engineer Stuti Jain and graduate students Kriti Kumar and Sayantika Chatterjee.
What distinguishes this AI-driven tool is its unique integration of real-world clinical data with molecular-level inputs. Unlike conventional rule-based systems or static drug-resistance databases, the model learns from actual treatment patterns by analysing curated antibiotic usage guidelines sourced from leading Indian hospitals. These clinical insights are then cross-referenced with bacterial genomic data and the chemical structures of antibiotics to uncover lesser-known but potentially effective alternatives.
The system was put to the test using case studies of multidrug-resistant pathogens, including:
- Klebsiella pneumoniae – a common cause of hospital-acquired pneumonia and bloodstream infections;
- Neisseria gonorrhoeae – the bacterium behind gonorrhea, increasingly resistant to standard treatments;
- Mycobacterium tuberculosis – the causative agent of tuberculosis, a major health concern in countries like India.
In all instances, the AI system successfully identified antibiotics with either proven efficacy or strong repurposing potential. These recommendations were then validated using known resistance data and expert evaluations, ensuring clinical relevance.
“This is an excellent example of how AI and international collaboration can come together to solve real-world medical challenges,” said Dr. Majumdar. “Our method makes it possible to use existing knowledge more effectively and opens the door to smarter, faster responses to AMR.”
Beyond its immediate utility, the AI tool offers scalable applications across healthcare systems. It can be embedded into hospital networks or national public health frameworks, especially in regions with limited diagnostic resources, to guide timely and effective treatment decisions while reinforcing antibiotic stewardship.
The researchers envision this solution becoming an integral part of modern infection management protocols. By bridging the gap between clinical experience and molecular data, the technology represents a vital advancement in the fight against AMR and a testament to the power of global research partnerships in addressing pressing health challenges.
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