He won a Nobel here for AlphaFold. Then he left. - John Jumper

**John Jumper** (0:00)
I don't really love the bitter lesson as people try and apply it. In fact, AlphaFold 2 is the opposite of that.

**SPEAKER_2** (0:05)
Protein folding is one of these Holy Grail type problems in biology.

**John Jumper** (0:11)
We predict nature level science with the press of a button in a very narrow category of nature level science of the structure of a specific protein.

**Tim Scarfe** (0:20)
John Jumper led the team behind AlphaFold, the system that predicted 200 million protein structures. In 2024, he won the Nobel Prize for Chemistry. And now, Jumper is leaving DeepMind.
But what did AlphaFold solve? What remains unsolved? And could AlphaFold be the template for AI for science?

**John Jumper** (0:46)
We are not trying to tell you everything. We are not a model of the entire cell. You try it, you measure nine times out of ten, you find out you are wrong, right? If you are wrong nine times out of ten, you are a very successful machine learner. You are incredibly productive.

**Tim Scarfe** (1:04)
So for half a century, structural biology had a massive bottleneck. DNA was easy to read, but protein structures were not.
A protein structure begins as a chain of amino acids. And then, often with help from the cell, they settle into a three-dimensional shape. And that shape determines what it binds, what chemistry it catalyzes, where it sits in the cell, and whether it even works at all. But from a machine learning perspective, if you only have the sequence, can you predict the fold? Can you predict the structure?

**SPEAKER_4** (1:44)
We've discovered more about the world than any other civilization before us.
But we have been stuck on this one problem. How do proteins fold up?

**Tim Scarfe** (1:55)
So every two years, there's a big scientific experiment called Casp.
Essentially, teams from around the world gather to see if they can predict protein structure from sequences based on recently done but not yet publicly available experiments. So for many decades, the progress was incremental until 2020, when John Jumper's team, AlphaFold, they produced a result which was significantly better than the competition. For many single-chain targets, the predictions from AlphaFold were so close to the targets, that the organizers of the event said that the problem had been essentially solved. So a protein structure that might have taken a year of specialist work, can now be predicted and operationalized in minutes.

**SPEAKER_2** (2:45)
Good job, everyone, the whole team, it's been an incredible effort.

**John Jumper** (2:50)
Congratulations on this work, it is really outstanding.

**SPEAKER_2** (2:53)
AlphaFold represents a huge leap forward that I hope will really accelerate drug discovery and help us to better understand disease. It's mind-blowing.

**SPEAKER_4** (3:02)
You know, these results were, for me, having worked on this problem so long, after many, many stops and starts, and will this ever get there, suddenly, this is a solution. We'd solve the problem.

**Tim Scarfe** (3:15)
And fair play to DeepMind. So they could have kept this close to their chest, but they decided to release it. They released a database with over 200 million predicted protein structures. Now, it's important to emphasize the word predicted. These are not experiments, but these are the basis for a lot of interesting new search work that the world of biology can now perform. And today, AlphaFold is now used by more than 3 million people in over 190 countries. So in 2024, the Nobel Prize committee made a formal verdict.
Half of the chemistry prize went to David Baker for computational protein design, and the other half went to Demis Hesabis and John Jumper for protein structure prediction. So AI had become a new tool for chemists, a way of seeing molecular structure at a level of resolution that structural biologists couldn't even have dreamed of before.

**John Jumper** (4:13)
So it's absolutely wonderful to be here. It's truly an extraordinary honor to tell you about this work, to tell you about the work of our team in protein structure prediction. So by about 1030, I said, oh, well, I guess not this year. And I told my wife and she goes, no, no, wait. And as she's telling me to wait, my phone lights up with a phone call from Sweden.
And thankfully, it was not the world's meanest prank call.

**Tim Scarfe** (4:42)
And all of this makes John's next move very interesting. So just a few days ago, he announced his departure at Google and he's going to Anthropic. It's important to note that John Jumper was not building generic prediction architectures like Claude or like Gemini. It was extremely structured, designed and engineered for the purpose of doing a specific thing.