Join us on Monday, February 10th for this year's Bloch lecture given by John Jumper, who won the 2024 Nobel Prize in Chemistry for his development of Alphafold2, an AI model that predicts the structures of proteins and nanostructures, released Google DeepMind.
Jumper is a University of Chicago Department of Chemistry alum (PhD ’17). He was advised by Profs. Karl Freed and Tobin Sosnick and afterward worked as a postdoctoral researcher in Sosnick’s lab before moving to Google DeepMind. He is the youngest chemistry laureate in over 70 years to be awarded the Nobel Prize. In September, Time Magazine recently named Jumper as one of the “100 Most Influential People in AI”.
***This lecture is free and open to the public***
Time
3:45pm - 5:00pm CST
Location
Kent Chemical Laboratory, Room 107
Zoom Link: TBA
***Directly following the symposium, a reception will be held in the Atrium of the Gorden Center for Integrative Science from 5:00-6:00 P.M. All attendees are welcome***
"Predicting the universe of biomolecular interactions with artificial intelligence"
The incredible diversity and complexity of proteins and their interactions underpins the biological processes of the cell, and computational predictions of these interactions are necessary to support the work of molecular biologists in understanding protein function and disease. In this talk, I will discuss our work in developing AlphaFold, an AI system that is capable of predicting novel interactions at unprecedented accuracy. In particular, I will discuss how we incorporated biological insights into the design of the AI system which dramatically increased the ability of the training process to learn biological and physical principles from experimental structural data.
In addition, the high accuracy of AlphaFold 2 in predicting protein structures and protein-protein interactions also raised the question of how to extend the success of AlphaFold to general biomolecular modeling, including protein-nucleic and protein-small molecule structure predictions as well as the effects of post-translational modification. I will discuss our latest work on AlphaFold 3 to develop a single deep learning system that makes accurate predictions across these interaction types, as well as examine some of the remaining challenges in predicting the universe of biologically-relevant protein interactions.
John Jumper received his PhD in Chemistry in 2017 from the University of Chicago, where he developed machine learning methods to simulate protein dynamics. He also holds an MPhil in Physics from the University of Cambridge and a B.S. in Physics and Mathematics from Vanderbilt University. Previously, John worked at D.E. Shaw Research on molecular dynamics simulations of protein dynamics and supercooled liquids. At Google DeepMind, John led the development of AlphaFold and is developing new methods to solve biological problems with AI. John has won numerous awards for his work on AlphaFold, including the Lasker Award, Breakthrough Prize in Life Sciences, the Canada Gairdner International Award, the BBVA Foundation Frontiers of Knowledge Award, and the 2024 Nobel Prize in Chemistry
Additional Reading:
Can We AlphaFold Our Way Out of the Next Pandemic? - Journal of Molecular Biology
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