Artificial intelligence has its Nobel Prize moment.
On Tuesday, the Nobel Prize in Physics went to a pair of longtime AI pioneers in Geoffrey Hinton and John Hopfield. Then, on Wednesday, the committee awarded the Nobel Prize in Chemistry to David Baker, Demis Hassabis and John Jumper — three scientists leading the way in using AI to predict and design proteins.
Baker, Hassabis and Jumper’s research is behind the creation of some of biotech’s most ambitious startups. That includes Isomorphic Labs, where Hassabis is CEO; and Xaira Therapeutics, which debuted earlier this year with over $1 billion in funding with Baker as a co-founder.
The awards reflect the incredibly rapid rise of AI in scientific research — especially the prize going to Hassabis and Jumper, who created AlphaFold, an AI model that predicts protein structures. The Nobel committee sometimes has a reputation for awarding work many years later, waiting to see if a breakthrough has stood the test of time. AlphaFold’s impact on science is still early — and arguably unclear — coming less than four years since the debut of AlphaFold2, a model that quickly predicts the three-dimensional shapes of proteins with unprecedented accuracy. It’s one of the fastest turnarounds in Nobel history in the short timespan from breakthrough to Nobel Prize.
But the award “shows the impact of AI on chemistry and medicine and the Nobel Committee clearly understands the impact of AI on everything,” ARCH managing director Bob Nelsen, a key investor in Xaira, wrote in a text. “This is just the beginning.”
Hassabis, 48, co-founded DeepMind in 2010, chasing a long-term vision of achieving artificial general intelligence. The project began by training AI models to play board games. Google acquired the startup in 2014. On a flight back from Seoul in 2016, as his team celebrated their AI system beating a world-class player at the board game Go, Hassabis said he realized the technology was now ready to take on bigger challenges.
A couple of years later, DeepMind unveiled AlphaFold by entering a long-running competition where research groups attempted to predict the shape and structure of proteins. Determining the structure of a protein has long been a key challenge to figuring out the role of a protein and designing more targeted drugs. At the time, the winning scores had plateaued for years, hovering around 40% accuracy. AlphaFold won with about 60% accuracy.
The true breakthrough, though, didn’t come until late 2020, after Hassabis and Jumper rebuilt AlphaFold. The new version — AlphaFold2 — lapped the field and led to claims that DeepMind had solved the protein-folding problem. Less than four years after that moment, Hassabis and Jumper have won the Nobel, as the DeepMind chief is now charging into drug R&D.
In an exclusive interview last year with Endpoints News, Hassabis said drug discovery is “the number one thing I always wanted to apply AI to once it was sophisticated enough and powerful enough.” In 2021, he founded Isomorphic Labs, where he remains CEO in addition to leading Google DeepMind.
Isomorphic signed partnerships with Novartis and Eli Lilly earlier this year and unveiled AlphaFold 3 in May, predicting far more complex structures than single proteins. AlphaFold 3 can predict the structures and complexes of proteins, DNA and RNA strands, and small molecules among other biomolecules.
What’s possible next
At a Wednesday press conference after the prize was announced, Hassabis said Isomorphic’s work is “progressing phenomenally well” beyond AlphaFold, exploring areas like designing compounds, predicting binding strength of molecules, and predicting properties like toxicity.
“We think there’s huge potential there to revolutionize, actually, the way drug discovery is done and try to shorten it down from almost a decade or more of work, usually, to get a drug all the way through out into the world, to instead of years, maybe months,” Hassabis said. “That could well be possible in the next few years once we continue to develop these technologies further.”
Jumper said he’s excited by AlphaFold’s relevance to the earliest stages of research, particularly in identifying biological targets. More than two million researchers have used AlphaFold to date, and about 30,000 scientific papers have cited their use of AlphaFold, he added.
“It just really shows that we’re going to start to get good at biology, both on the therapeutics side and the understanding of disease,” Jumper said at the news conference. “The moment I will be almost as excited as this is the Nobel Prize that talks about the work done with AlphaFold to understand this disease.”
Baker, for his part, has grown his Seattle laboratory into a powerhouse since founding the Institute for Protein Design in 2012. He began working on a computer model called Rosetta in the late 1990s to predict protein structures. But his lab’s recent success has come from a willingness to embrace cutting-edge AI ideas, like the transformer-based model, after AlphaFold2’s triumph in 2020.
His lab has published a torrent of papers over the last few years on creating de novo proteins that look more and more like the leads of potential drug candidates. It has also become a prolific startup creator: Baker has co-founded 21 companies over his career, more than half of those in the past five years. That includes companies at the forefront of AI in biotech like Xaira, Charm Therapeutics, Vilya and Monod Bio.
Beyond their corporate ventures, the newly-minted Nobel laureates have had outsized impact in shaping the thinking of the broader AI bio field. Countless startups have launched with the elevator pitch of making “AlphaFold-for-X.” Another slew of young companies, like EvolutionaryScale, Chai Discovery, Iambic Therapeutics and Profluent Bio, have built and use their own protein structure prediction models that compete with DeepMind’s AlphaFold and Baker’s RoseTTAFold.
Nobel Prize selections are often supported with a story of how that breakthrough translates to the real world. Recent winners like CRISPR/Cas9 and mRNA, for instance, have produced successful, transformative medicines. In bowing to the AI trend of 2024, Stockholm is placing its own bet that AI’s potential will pay off. Even the newest winners see this as just the start of a longer journey.
“I hope that when we look back on AlphaFold, it will just be the first proof point of AI’s incredible potential to accelerate scientific discovery,” Hassabis said.