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A trio of scientists consisting of Demis Hassabis, co-founder and CEO of Google’s AI division DeepMind, as well as John Jumper, Senior Research Scientist at Google DeepMind and David Baker of the University of Washington have been awarded the 2024 Nobel Prize in Chemistry for their groundbreaking work in predicting and developing new proteins.
The DeepMinders won for AlphaFold 2, an AI system capable of predicting the 3D structure of proteins from their amino acid sequences. Meanwhile, Baker won for leading a laboratory where the 20 amino acids that form proteins were used to design new ones, including proteins for “pharmaceuticals, vaccines, nanomaterials and tiny sensors,” according to the Nobel committee’s announcement.
The award highlights how artificial intelligence is revolutionizing biological science — and comes just one day after what I believe to be the first Nobel Prize awarded to an AI technology, that one for Physics to fellow Google DeepMinder Geoffrey Hinton and Princeton professor John J. Hopfield, for their work in artificial neural networks.
The Royal Swedish Academy of Sciences announced the prize as it did with the Physics one, valued at 11 million Swedish kronor (around $1 million USD), split among the laureates — half will go to Baker and the other half divided again in fourths of the total to Hassabis and Jumper.
The committee emphasized the unprecedented impact of AlphaFold, describing it as a breakthrough that solved a 50-year-old problem in biology: protein structure prediction, or how to predict the three-dimensional structure of a protein from its amino acid sequence.
For decades, scientists knew that a protein’s function is determined by its 3D shape, but predicting how the string of amino acids folds into that shape was incredibly complex. Researchers had attempted to solve this since the 1970s, but due to the vast number of possible folding configurations (known as Levinthal’s paradox), accurate predictions remained elusive.
AlphaFold, developed by Google DeepMind, made a breakthrough by using AI to predict the 3D structures of proteins with near-experimental accuracy, meaning that the predictions made by AlphaFold for a protein’s 3D structure are so close to the results obtained from traditional experimental methods—like X-ray crystallography, cryo-electron microscopy, or nuclear magnetic resonance (NMR) spectroscopy—that they are almost indistinguishable.
When AlphaFold achieved “near-experimental accuracy,” it was able to predict protein structures with a level of precision that rivaled these methods, typically within an error margin of around 1 Ångström (0.1 nanometers) for most proteins. This means the model’s predictions closely matched the actual structures determined by experimental means, making it a transformative tool for biologists.
Hassabis and Jumper’s work, developed at DeepMind’s London laboratory, has transformed the fields of structural biology and drug discovery, offering a powerful tool to scientists worldwide.
“AlphaFold has already been used by more than two million researchers to advance critical work, from enzyme design to drug discovery,” Hassabis said in a statement. “I hope we’ll look back on AlphaFold as the first proof point of AI’s incredible potential to accelerate scientific discovery.”
AlphaFold’s Global Impact
AlphaFold’s predictions are freely accessible via the AlphaFold Protein Structure Database, making it one of the most significant open-access scientific tools available. Over two million researchers from 190 countries have used the tool, democratizing access to cutting-edge AI and enabling breakthroughs in fields as varied as molecular biology, drug development, and even climate science.
By predicting the 3D structure of proteins in minutes—tasks that previously took years—AlphaFold is accelerating scientific progress. The system has been used to tackle antibiotic resistance, design enzymes that degrade plastic, and aid in vaccine development, marking its utility in both healthcare and sustainability.
John Jumper, co-lead of AlphaFold’s development, reflected on its significance, stating, “We are honored to be recognized for delivering on the long promise of computational biology to help us understand the protein world and to inform the incredible work of experimental biologists.” He emphasized that AlphaFold is a tool for discovery, helping scientists understand diseases and develop new therapeutics at an unprecedented pace.
The Origins of AlphaFold
The roots of AlphaFold can be traced back to DeepMind’s broader exploration of AI.
Hassabis, a chess prodigy, began his career in 1994 at the age of 17, co-developing the hit video game Theme Park, which was released on June 15 that year.
After studying computer science at Cambridge University and completing a PhD in cognitive neuroscience, he co-founded DeepMind in 2010, using his understanding of chess to raise funding from famed contrarian venture capitalist Peter Thiel. The company, which specializes in artificial intelligence, was acquired by Google in 2014 for around $500 million USD.
As CEO of Google DeepMind, Hassabis has led breakthroughs in AI, including creating systems that excel at games like Go and chess.
By 2016, DeepMind had achieved global recognition for developing AI systems that could master the ancient game of Go, beating world champions. It was this expertise in AI that DeepMind began applying to science, aiming to solve more meaningful challenges, including protein folding.
The AlphaFold project formally launched in 2018, entering the Critical Assessment of protein Structure Prediction (CASP) competition—a biannual global challenge to predict protein structures. That year, AlphaFold won the competition, outperforming other teams and heralding a new era in structural biology. But the real breakthrough came in 2020, when AlphaFold2 was unveiled, solving many of the most difficult protein folding problems with an accuracy previously thought unattainable.
AlphaFold 2’s success marked the culmination of years of research into neural networks and machine learning, areas in which DeepMind has become a global leader.
The system is trained on vast datasets of known protein structures and amino acid sequences, allowing it to generalize predictions for proteins it has never encountered—a feat that was previously unimaginable.
Earlier this year, Google DeepMind and Isomorphic Labs unveiled AlphaFold 3, the third generation of the model, which the creators say uses an improved version of the Evoformer module, a deep learning architecture that was key to AlphaFold 2’s remarkable performance.
The new model also incorporates a diffusion network, similar to those used in AI image generators, which iteratively refines the predicted molecular structures from a cloud of atoms to a highly accurate final configuration.
David Baker’s Contribution to Protein Design
While Hassabis and Jumper solved the prediction problem, David Baker’s work in de novo protein design offers an equally transformative approach: the creation of entirely new proteins that do not exist in nature.
Based at the University of Washington’s Institute for Protein Design, Baker’s lab developed Rosetta, a computational tool used to design synthetic proteins.
Baker’s work has led to the development of proteins that could be used to create novel therapeutics, including custom-designed enzymes and virus-like particles that may serve as vaccines. His group has even designed proteins to detect fentanyl, an opioid at the center of a global health crisis.
By designing new proteins from scratch, Baker’s research expands the boundaries of what proteins can do, complementing the predictive power of AlphaFold by enabling the creation of molecules tailored to specific functions.
The Future of AI in Science
The Nobel Prize recognition of AlphaFold and Baker’s work underscores a broader trend: AI is rapidly becoming an indispensable tool in scientific research. AlphaFold’s success has sparked new interest in the potential of AI to solve complex problems across various fields, including climate change, agriculture, and materials science.
The Nobel Committee highlighted the transformative potential of these discoveries, emphasizing that they “open up vast possibilities” for the future of biology and chemistry. Hassabis has long been vocal about AI’s potential to drive innovation, but he is also clear-eyed about the risks. “AI has the potential to accelerate scientific discovery at a rate we’ve never seen before, but it’s crucial that we use it responsibly,” he said in a recent interview.
As AI systems like AlphaFold continue to evolve, their ability to simulate biological processes and predict outcomes could revolutionize healthcare, sustainability efforts, and beyond. Jumper and Hassabis’ Nobel Prize is a recognition of their work’s enormous impact, but it also signals the dawn of a new era in science—one where AI plays a central role in unlocking the mysteries of life.
What’s next?
The 2024 Nobel Prize in Chemistry recognizes the profound contributions of Demis Hassabis, John Jumper, and David Baker, whose pioneering work has reshaped the landscape of protein science. AlphaFold, now a cornerstone tool for researchers worldwide, has accelerated discovery in ways previously unimaginable.
David Baker’s work in computational protein design further expands the possibilities for biological innovation, offering new solutions to global challenges.
Together, these advancements mark the beginning of a new era for artificial intelligence in science—one where the possibilities are just beginning to unfold (pun intended).
While he remains optimistic about AI’s positive impact, Hassabis warns that the risks, including the potential for societal-scale disasters, must be taken as seriously as the climate crisis.
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