Name: Biochemistry and AI
Start: 2026-03-28T14:00:00+00:00
End: 2026-03-28T15:30:00+00:00
Location: Department of Biochemistry, Sanger Building, University of Cambridge

Join us for three talks on how AI can be applied to big questions in Biochemistry. The talks will be followed by a Q and A panel discussion.
About this Event

This event features three talks by Susanne Bornelöv, Attila Tokoli and Michael Boemo on how artificial intelligence can be applied to big questions in Biochemistry, such as: how can AI help us understand life, reveal patterns in biological data, assist in creating enzymes to break down plastics and improve cancer treatment. The talks will be followed by a Q and A panel discussion.

In her talk entitled Learning the rules of life in the age of AI, Susanne Bornelöv (Department of Biochemistry) will explore how computers can help us to understand life. By teaching computers to find patterns in biological data, we can run virtual experiments and explore ideas faster than ever, taking us closer to understanding how living systems really work.

Attila Tokoli’s (Department of Biochemistry) talk is entitled Teaching computers to speak biology: What ChatGPT’s cousin is doing in the lab. Proteins are nature’s molecular machines, built from a 20-letter alphabet that folds into complex 3D shapes. Just as ChatGPT learned to understand human language, new AI systems are learning to ‘read’ and ‘write’ proteins by training on millions of examples from nature. The talk will explore how these AI tools change our understanding of proteins and help us to create entirely new enzymes that can break down plastics.

Michael Boemo’s (Department of Pathology) talk is entitled Learning how life copies itself: Developing AI that tracks genome replication. In the time it takes you to read this sentence, the cells in your body will copy about 20,000 miles of DNA. Genome replication is the target of multiple chemotherapies, including several that have been developed in Cambridge, but understanding which cancer cells are susceptible and resistant to these therapies is a pressing problem. Michael will describe our development of AI models that track genome replication and uncover new clues for improving cancer treatment.