University of Chicago Ph.D. Grad won the Nobel Prize in chemistry 2024 for Game-Changing AI for protein prediction

The Accidental Chemist Who Won Nobel Prize: When John Jumper began his Ph.D. journey at the University of Chicago in 2012, he didn’t just take the road less traveled—he created his path. The future Nobel Prize winner’s journey to scientific renown is a story of unexpected turns, curiosity, and an unshakable determination to tackle one of biology's toughest challenges: understanding how proteins fold.

Fast forward to 2024, Jumper, now a celebrated scientist, stands as a pioneer in the field of computational biology. He is recognized for co-leading the development of AlphaFold. This revolutionary artificial intelligence program cracked the code of protein folding, earning him a share of the Nobel Prize in Chemistry.

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But Jumper’s journey wasn’t a straight line. In fact, it almost didn’t happen.

A Rocky Start

“When I started, I knew no chemistry. None,” Jumper recalls with a laugh. “I had to speedrun the whole thing.” This unorthodox start saw him frantically staying a week ahead of the undergrad general chemistry course he was assigned to teach as a teaching assistant.

Jumper’s initial love was physics, which he pursued during his undergraduate years. However, when his first attempt at a Ph.D. in physics failed to spark joy, he stepped away and found himself drawn to computational biology. He took a job at a company that used computer programs to model proteins, and the problem of protein folding hooked him instantly.

“It was so easy to see the connections,” Jumper says. “If we do this right, someone goes home from the hospital.”

Determined to follow this calling, he applied to the University of Chicago’s chemistry Ph.D. program. It was an unusual choice for someone with little chemistry experience, but it turned out to be exactly what Jumper needed to bring his multidisciplinary vision to life.

The Knotty Problem of Protein Folding

Proteins are the molecular machines of life, responsible for tasks ranging from transporting oxygen in the blood to enabling thoughts in the brain. Each protein is made up of a unique combination of 20 amino acids, and its function is determined by the way it folds into a precise three-dimensional shape.

For decades, scientists could sequence the amino acids in a protein, but predicting how it would fold was another story. The potential ways a protein could twist and turn outnumber the stars in the sky. Without knowing a protein’s structure, it’s nearly impossible to understand its role or design drugs to interact with it.

Traditional methods, like X-ray crystallography, were labor-intensive and expensive. Despite the promises of modern computation, no algorithm had managed to predict protein folding—until AlphaFold reliably.

Interdisciplinary Science in Action

At the University of Chicago, Jumper joined the lab of Profs. Karl Freed and Tobin Sosnick are two scientists who combined theoretical and experimental approaches to solve biological problems. This was a perfect fit for Jumper, who described himself as an “accidental chemist” eager to learn from diverse perspectives.

“I’m a big believer in interdisciplinary science,” he says. “You learn so much more by talking to a diverse group of experts to learn what they’re excited about and how they think about things.”

Jumper’s Ph.D. work focused on creating machine-learning models to simulate the process of protein folding. Though distinct from AlphaFold’s goal of predicting protein structure directly from genetic sequences, his work laid the foundation for his later breakthroughs. He also spent a short stint in a wet lab, where he learned the challenges experimental scientists face—a perspective that would later inform his computational work. “Even if I only held a pipette for three months, I benefitted,” Jumper jokes.

The Birth of AlphaFold

After earning his Ph.D. in 2017, Jumper joined Google’s DeepMind, a hub for cutting-edge artificial intelligence research. There, he teamed up with Demis Hassabis, another future Nobel Laureate, to tackle the decades-old problem of predicting protein structures.

The result was AlphaFold, an AI program that uses deep learning to predict a protein’s 3D shape from its amino acid sequence with unprecedented accuracy. Drawing on a database of over 200,000 known protein structures, AlphaFold applies insights from these examples to new sequences, providing not only predictions but also confidence estimates for its results.

“It hit the level where it transformed what people thought was possible,” says Sosnick.

Since its release as an open-source program in 2021, AlphaFold has revolutionized the field. Scientists have used it to study proteins involved in diseases like Alzheimer’s, design potential new drugs, and even understand viral evolution. The program’s impact extends to environmental science, where researchers explore proteins that could break down pollutants.

The Impact of a Breakthrough

AlphaFold is not perfect—scientists still rely on traditional methods to confirm some structures—but its speed and accessibility have made it an indispensable tool. Thousands of studies have already cited AlphaFold, and its database of predicted protein structures continues to grow.

The Nobel Committee for Chemistry hailed AlphaFold as the first major scientific breakthrough driven by artificial intelligence, cementing its place in history. Jumper shares the 2024 Nobel Prize with Hassabis and David Baker, whose work focused on designing genetic sequences to create desired protein structures.

A Legacy of Collaboration

Back at the University of Chicago, Jumper’s influence endures. The code he wrote during his Ph.D. is still in use, and Sosnick’s lab recently received funding to expand its applications. Sosnick fondly remembers Jumper as a generous and collaborative student who was always eager to help others with their projects. “He’s the graduate student that keeps on giving,” Sosnick says with a smile.

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As Jumper prepares to receive his Nobel Prize in Sweden, he remains optimistic about the future of AI in science. “Nature is complex, and I think neural networks can handle complexity in surprisingly useful ways,” he says. “I’m interested to see how far it can be pushed.” For the accidental chemist who changed the game, the possibilities are endless. For more global updates, stay tuned with the Education Post News.