A life in context
The person behind the prize
Jumper trained in physics and chemistry and completed his doctorate at the University of Chicago in 2017. He joined DeepMind and became a central scientific leader of AlphaFold. His work combines an understanding of molecular structure with new machine-learning architectures.
The contribution
He helped lead the design of AlphaFold2, which reasons about relationships within a protein’s sequence and its three-dimensional geometry. Its predictions can guide experiments, though confidence and biological context still matter.
The idea in plain language
Proteins are chains of amino acids that fold into intricate shapes. Those shapes help determine what the molecules do. Predicting a shape from its sequence had been a major challenge for decades.
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