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New Delhi: In a boost to scientific advancement aimed at curbing the global threat of drug-resistant infections, researchers from the Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi) and France’s Inria Saclay have developed an artificial intelligence (AI)-based system that can recommend effective combinations of existing antibiotics to fight superbugs.
Superbugs are germs like bacteria and fungi that cause hard-to-treat infections. Most superbugs are bacteria that have developed antibiotic resistance – the ability to survive antibiotic medications.
The project, jointly led by Professor Angshul Majumdar and Dr Emilie Chouzenoux, is part of a broader India-France research collaboration between Deep Light (Delhi) and CentraleSupelec, a French engineering school, said an official statement of IIIT-D.
The team includes engineer Stuti Jain and graduate researchers Kriti Kumar and Sayantika Chatterjee, it added.
“This is an excellent example of how AI and international collaboration can come together to solve real-world medical challenges and our method makes it possible to use existing knowledge more effectively and opens the door to smarter, faster responses to antimicrobial resistance (AMR),” Prof. Majumdar told PTI.
AMR occurs when bacteria adapt to antibiotics, rendering them ineffective. Prof. Majumdar said the misuse of antibiotics, particularly in countries like India, is a major contributor to the crisis.
“We often take antibiotics even for viral infections, which don’t require them. Over time, bacteria evolve and adapt. As a result, even simple infections like urinary tract infections or wounds can become resistant to treatment,” he added.
The AI system that the team developed goes far beyond traditional rule-based models. It analyzes real-world clinical decisions from leading Indian hospitals and combines this with bacterial genome data and the chemical structure of antibiotics to recommend optimal treatment options.
The system was successfully tested on multi-drug resistant strains like Klebsiella pneumoniae, Neisseria gonorrhoeae and Mycobacterium tuberculosis.
Prof. Majumdar shared a harrowing case from AIIMS, Kalyani, where a young patient’s artificial hip joint became infected and was unresponsive to even last-resort antibiotics.
“It was a simple muscle infection, but it became untreatable. The patient from a poor family was left bedridden and without options,” he said. “This is the reality of AMR – it can devastate lives even when the infection seems ordinary.”
The professor said the AI model offers combination therapy suggestions, mirroring how doctors treat advanced cases today.
“Instead of recommending one antibiotic, our system can propose a cocktail of drugs based on the genome sequence of the bacteria,” said Majumdar.
“Doctors can ask for five or ten possible options for a specific strain and the AI suggests viable treatments,” he added.
While the model is currently focused on bacterial infections, it can be retrained to tackle viral infections or even lifestyle diseases like hypertension, where drug resistance is emerging.
“Our core motivation was to address infectious diseases – a problem that plagues countries in Southeast Asia, Africa and Latin America far more than it does the West,” said Majumdar.
“There’s less funding for this kind of work, but the impact is massive,” he added.
The team hopes that their model would eventually be embedded in hospital systems and public health frameworks, particularly in regions with limited access to advanced diagnostics.
With AMR recognized as one of the most pressing global health threats of the 21st century, this AI-powered solution may offer doctors a timely, data-driven ally in the battle to preserve the effectiveness of antibiotics.
