Google DeepMind AI scientists win Nobel Chemistry Prize for protein model

Google DeepMind AI scientists win Nobel Chemistry Prize for protein model

Google DeepMind's logo set atop protein structures uncovered by its AlphaFold2 AI model

Google DeepMind’s Demis Hassabis and John Jumper have won the 2024 Nobel Prize in Chemistry for their work using AI to uncover the complex structure of almost every protein known to science.

Hassabis and Jumper shared the Nobel Prize in Chemistry with David Baker, a computational biochemist who designed new proteins for use in pharmaceuticals, vaccines, and nanomaterials.

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“One of the discoveries being recognised this year concerns the construction of spectacular proteins. The other is about fulfilling a 50-year-old dream: predicting protein structures from their amino acid sequences. Both of these discoveries open up vast possibilities,” said Heiner Linke, chair of the Nobel Committee for Chemistry.

In 2020, Hassabis, Jumper and the team at Google DeepMind developed AlphaFold2, an AI model capable of predicting protein structures. Two years later, the model was used to predict the structure of nearly all of the 200 million proteins known to science.

Google DeepMind has open sourced AlphaFold2, providing researchers worldwide with access to an AI tool capable of helping to better understand antibiotic resistance and create images of enzymes that can decompose plastic.

The Nobel Committee for Chemistry said the ability to predict protein structures using an AI system like AlphaFold2 “confers the greatest benefit to humankind.”

The winners will share 11 million Swedish kronor (US$1.05 million), with one half going to Baker and the other jointly between Hassabis and Jumper.

Previous Nobel Chemistry prize winners include Ernest Rutherford, Marie Curie (who also won the Physics prize), and Emmanuelle Charpentier.

"Receiving the Nobel Prize is the honour of a lifetime,” said Hassabis. “AlphaFold has already been used by more than two million researchers to advance critical work, from enzyme design to drug discovery. I hope we'll look back on AlphaFold as the first proof point of AI's incredible potential to accelerate scientific discovery.”

“Computational biology has long held tremendous promise for creating practical insights that could be put to use in real-world experiments,” Jumper said.

“AlphaFold delivered on this promise. Ahead of us [is] a universe of new insights and scientific discoveries made possible by the use of AI as a scientific tool. Thank you to my colleagues over the years, for making possible this moment of recognition, as well as the many moments of discovery that lie ahead.”

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