The Royal Swedish Academy of Sciences has awarded a half share of the 2024 Nobel Prize For Chemistry to Demis Hassabis, CEO of Google DeepMind and his colleague John Jumper for "protein structure prediction", referring to the breakthroughs made by their AI Model, AlphaFold.

The other half goes to David Baker, Professor of Biochemistry at the University of Washington School of Medicine and Director of the UW Medicine Institute for Protein Design, “for computational protein design”.

After the previous day's news that AI godfather Geoffrey Hinton was sharing the Physics Nobel Prize for Physics, it was something of a double whammy when it was announced that AI-powered research was again being recognized by the Chemistry Prize. Here is what the official statement tells us: 

The Nobel Prize in Chemistry 2024 is about proteins, life’s ingenious chemical tools. David Baker has succeeded with the almost impossible feat of building entirely new kinds of proteins. Demis Hassabis and John Jumper have developed an AI model to solve a 50-year-old problem: predicting proteins’ complex structures. These discoveries hold enormous potential.

Pictured on the left, Sir Demis Hassabis, born in London, UK on July 27, 1976 and on the right, John Jumper, born in Little Rock, Arkansas in 1985, making him the youngest chemistry laureate for over 70 years. Both are currently affiliated with Google DeepMind, London, United Kingdom and are Nobel Laureates in Chemistry in recognition of their breakthroughs with AlphaFold, a successor to AlphaGo, the pioneering deep learning model.

Hassabis was an original co-founder in of DeepMind and first came to our attention in 2014 when we reported Google Buys Unproven AI Company, Deep Mind: 

Google seems to be serious about getting a slice of the AI business. It has now bought the UK start-up DeepMind Technologies for a very large sum of money, even though the company hasn't actually done anything very much yet.

DeepMind had been was founded in London in 2010 Shane Legg, Mustafa Suleyman and Demis Hassabis who, according to Mike James, was:

"the one of the three with a larger than life persona. He was a child chess prodigy and has been involved in games of all types ever since. Working in the games industry, he created some well known games such as Theme Park. He then  studied computer science at Cambridge and went to on to get a PhD in Neuroscience from University College London in 2009. His best known research isn't so much in the area of AI as experimental neurology - a study of the way that amnesia due to damage to the hippocampus effects the ability to imagine the future."

It was exactly two years later that we reported Google's AI Beats Human Professional Player At Go, introducing DeepMind's revolutionary network, called AlphaGo, which used two distinct neural networks in a traditional reinforcement learning "actor-critic" arrangement. By March AlphaGo had beaten South Korea's Lee Se-dol, an 18-times world Go champion, in a 5-match challenge.  

Mike James wrote in Why AlphaGo Changes Everything:

We have a breakthrough moment in AI; one that most experts thought would take at least another ten years. An AI system has taught itself to play Go, one of the more subtle and human of games, and it has beaten the human world champion.

As I reported in AlphaGo Triumphs In China in 2017, Go was just the first domain that DeepMind had in mind for its AI as outlined in a this statement:

The research team behind AlphaGo will now throw their energy into the next set of grand challenges, developing advanced general algorithms that could one day help scientists as they tackle some of our most complex problems, such as finding new cures for diseases, dramatically reducing energy consumption, or inventing revolutionary new materials. If AI systems prove they are able to unearth significant new knowledge and strategies in these domains too, the breakthroughs could be truly remarkable.

By the end of 2018 we had seen how, with AlphaFold, the team had had used the methods developed for AlphGo to learn to predict protein folding. Commenting on this in AlphaFold DeepMind's Protein Structure Breakthrough, Mike James wrote:

This is not an obvious application of AI, but it is the sort of thing that could be revolutionary in many fields in the future. AI gives us the ability to solve problems in new ways - the only problem is we don't know how much to trust the results as AI predictions rarely come with error bounds. It also seems that the other competitors and onlookers were a bit shocked that an AI team could "gate crash" their party and take over a field of study without really knowing anything about it. This is likely to happen increasingly often in AI-lead science.

The specific research that led to the Nobel Prize in Chemistry was the 2020 publication High Accuracy Protein Structure Prediction Using Deep Learning, a paper with 30 authors, the lead author being John Jumper. Reporting this in AlphaFold Solves Fundamental Biology Problem, Mike James commented: 

This really is a breakthrough and you can expect new techniques and new products as a result. The point is that we have the recipes for proteins in the form of DNA sequences, but until now we didn't know what shape an amino acid sequence would have.  Now we know the recipe and can predict the end product. 

In a phone interview Adam Smith from the Nobel Prize website asked Demis Hassabis about the interplay between AI and individual scientists:

AS: ... AlphaFold, AlphaFold2, now AlphaFold3, ushers in a whole new world in science. How do you see the relationship between these tools and the individual scientist?

DH: The reason I’ve worked on AI my whole life is that I’m passionate about science and finding out knowledge, and I’ve always thought if we could build AI in the right way, it could be the ultimate tool to help scientists, help us explore the universe around us. I hope AlphaFold is a first example of that. 

AS: But in terms of how, where this leaves the individual, if you like, because the power is so extraordinary and just mind blowing. But there are still individual scientists asking individual questions. So what’s the interplay like?

DH: I think that, at least for the next foreseeable future, I feel like this allows individual scientists to do so much more. Because, these systems, they’re tools. They’re very good for analyzing data and finding patterns and structure in data. But, you know, they can’t, figure out what the right question is to ask, or the right hypothesis, or the right conjecture. All of that’s got to come from the human scientist. I think the best scientists paired with these kinds of tools will be able to do incredible things, perhaps even in smaller teams than they used to be able to, because, they can rely on the tools to do a lot of the legwork.

I find it very heartening that Hassabis views the AI as tools that can be relied on to do the gruntwork, and that it is the human scientists who are needed to come up with the questions to which we need the answers.

More Information

Nobel Prize In Chemistry

Related Articles

Geoffrey Hinton Shares Nobel Prize For Physics 2024 

Google Buys Unproven AI Company, Deep Mind

Why AlphaGo Changes Everything 

AlphaGo Triumphs In China

AlphaFold DeepMind's Protein Structure Breakthrough

AlphaFold Solves Fundamental Biology Problem

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