London School of Hygiene & Tropical Medicine

Antimicrobial resistance (AMR) is among the most pressing global health threats of the 21st century, with the potential to thrust modern medicine back into a pre-antibiotic era. Resistance can arise through diverse mechanisms, including genomic mutations that prevent antibiotics from reaching or acting on their targets. To limit the spread of AMR, surveillance systems must detect both known and emerging resistance markers. Nick Furnham will present our development of an AI-based protein large language model to predict mutations that drive resistance in key bacterial pathogens, including WHO priority organisms such as drug-resistant tuberculosis and Pseudomonas. This approach identifies resistance-associated regions and highlights new targets for surveillance. Nick will also describe our approach applying similar methods to norovirus, causative agent for acute gastroenteritis, to anticipate immune escape mutations, informing vaccine design. By predicting evolutionary changes, these tools support global efforts in AMR monitoring, outbreak preparedness, and therapeutic development.

In this interactive lecture, Robert Hughes will ask the question “How might AI change the practice of global public health (research)?”

After briefly recapping on the state of AI as we approach 2026, Robert will share some examples of the types of questions we might want to ask, collaborations we might need to form, and principles we might need to adopt to successfully navigate a complicated future co-existing with advanced AI tools. Along the way Robert will share some examples of how AI is generating not just new approaches to doing what we’ve always done, but also new ways of thinking about problems and new research questions.

Speakers

• Nick Furnham
• Robert Hughes

Event notices 

• Please note this event is in person only
• Please note that the recording link will be listed on this page when available