An AI and Agentic Framework for Mapping the E3 Ligase Interactome and Discovering Novel Cancer Therapeutics
Supervisors:
Dr Ke Yuan, School of Cancer Sciences
Prof Danny Huang, School of Cancer Sciences
Dr Jake Lever, School of Computing Science
Prof David Chang, School of Cancer Sciences
Summary:
Do you want to be at the forefront of the AI revolution in medicine? This project uses a team of AI scientists to tackle one of the biggest challenges in cancer therapy: discovering new drug targets within the vast, unexplored E3 ligase family.
You will lead a ""Virtual Lab"" of AI agents, guided by you, to systematically screen the entire human proteome. You will first enhance our state-of-the-art AI model, PLM-interact, to accurately predict E3-substrate interactions. Your team of AI agents will then use this model, alongside cutting-edge structural prediction tools, to discover and priorities novel interactions.
Finally, you will take the most exciting AI-driven discoveries to the wet lab to prove they are real. This is a unique opportunity to master the full cycle of modern drug discovery—from an AI hypothesis to a validated biological target—and to pioneer a new era of AI-human collaborative science.