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Nobel Prize in Chemistry 2024 - NobelPrize.org
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Relevant to AI safety discussions as a prominent example of transformative AI capability (AlphaFold2) solving a grand scientific challenge, illustrating both the potential and the pace of AI-driven breakthroughs in high-stakes domains like biology and medicine.
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Summary
The 2024 Nobel Prize in Chemistry was awarded for breakthroughs in computational protein science: one half to David Baker for computational protein design, and the other half jointly to Demis Hassabis and John Jumper for AlphaFold2, the AI system that solved the 50-year-old protein folding problem. This popular-science summary explains how these advances enable understanding and designing proteins at scale, with transformative implications for medicine, materials, and biology.
Key Points
- •AlphaFold2, developed by DeepMind's Demis Hassabis and John Jumper, can predict the 3D structure of virtually any protein from its amino acid sequence with high accuracy.
- •David Baker pioneered computational protein design, enabling creation of entirely new proteins not found in nature for therapeutic and industrial applications.
- •The protein folding problem—predicting structure from sequence—had stumped scientists for 50 years before AlphaFold2 effectively solved it using deep learning.
- •AlphaFold2 has predicted structures for over 200 million proteins, dramatically accelerating biological research and drug discovery worldwide.
- •These advances represent a landmark case of AI solving a fundamental scientific problem, raising both opportunities and questions about AI's role in scientific discovery.
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| Page | Type | Quality |
|---|---|---|
| Demis Hassabis | Person | 45.0 |
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[Popular science background:\\
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They have revealed proteins’ secrets through computing and artificial intelligence (pdf)](https://www.nobelprize.org/uploads/2024/11/popular-chemistryprize2024.pdf)
[Populärvetenskaplig information:\\
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De har avslöjat proteinernas hemligheter med hjälp av datorer och artificiell intelligens (pdf)](https://www.nobelprize.org/uploads/2024/11/popular-chemistryprize2024-swedish.pdf)
## The Nobel Prize in Chemistry 2024
Chemists have long dreamed of fully understanding and mastering the chemical tools of life – proteins. This dream is now within reach. **Demis Hassabis** and **John Jumper** have successfully utilised artificial intelligence to predict the structure of almost all known proteins. **David Baker** has learned how to master life’s building blocks and create entirely new proteins. The potential of their discoveries is enormous.
## They have revealed proteins’ secrets through computing and artificial intelligence
How is the exuberant chemistry of life possible? The answer to this question is the existence of proteins, which can be described as brilliant chemical tools. They are generally built from 20 amino acids that can be combined in endless ways. Using the information stored in DNA as a blueprint, the amino acids are linked together in our cells to form long strings.
Then the magic of proteins happens: the string of amino acids twists and folds into a distinct – sometimes unique – three-dimensional structure (Figure 1). This structure is what gives proteins their function. Some become chemical building blocks that can create muscles, horns or feathers, while others may become hormones or antibodies. Many of them form enzymes, which drive life’s chemical reactions with astounding precision. The proteins that sit on the surfaces of cells are also important, and function as communication channels between the cell and its surroundings.
© Johan Jarnestad/The Royal Swedish Academy of Sciences
It is hardly possible to overstate the potential encompassed by life’s chemical building blocks, these 20 amino acids. The Nobel Prize in Chemistry 2024 is about understanding and mastering them at an entirely new level. One half of the prize goes to Demis Hassabis and John Jumper, who have utilised artificial intelligence to successfully solve a problem that chemists wrestled with for over 50 years: predicting the three-dimensional structure of a protein from a sequence of amino acids. This has allowed them to predict the structure of almost all 200 million k
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