John Jumper

John M. Jumper
Born	Template:Birth year and age
Birthplace	Little Rock, Arkansas, United States
Nationality	American
Occupation	Computational biologist, research scientist
Employer	Google DeepMind
Known for	AlphaFold protein structure prediction
Education	PhD (University of Chicago)
Awards	Nobel Prize in Chemistry (2024)

John M. Jumper (born 1985) is an American chemist and computer scientist who serves as a senior research scientist at Google DeepMind in London, United Kingdom. He is best known for leading the development of AlphaFold, an artificial intelligence system capable of predicting the three-dimensional structures of proteins from their amino acid sequences with remarkable accuracy. For this work, Jumper was awarded a share of the 2024 Nobel Prize in Chemistry, alongside Demis Hassabis, the co-founder and CEO of Google DeepMind. Born in Little Rock, Arkansas, Jumper studied mathematics and physics at Vanderbilt University before completing graduate studies at the University of Chicago, where he earned both a master's degree and a doctorate. His path from theoretical physics to computational biology reflects a career shaped by intellectual curiosity and the conviction that artificial intelligence could help solve one of the longest-standing challenges in molecular biology — the protein folding problem. AlphaFold, and its successor AlphaFold 2, have since transformed structural biology and made predicted structures for hundreds of millions of proteins freely available to researchers worldwide.

Early Life

John M. Jumper was born in 1985 in Little Rock, Arkansas. Details about his family background and childhood remain largely private. In a recorded interview during Nobel Week in Stockholm in December 2024, Jumper discussed his early interest in science and the intellectual trajectory that led him to the intersection of physics, computation, and biology. Growing up in Arkansas, Jumper developed an affinity for the sciences and mathematics that would carry him through his undergraduate and graduate education and ultimately into one of the most consequential research programs in modern computational science.

Education

Jumper attended Vanderbilt University in Nashville, Tennessee, where he earned a Bachelor of Science degree in Mathematics and Physics in 2007. His dual concentration in mathematics and physics provided a rigorous quantitative foundation that would prove essential to his later work in computational biology and machine learning.

After completing his undergraduate studies, Jumper pursued graduate work at the University of Chicago. He received a Master of Science degree in 2012 and a Doctor of Philosophy degree in 2017. His doctoral research at UChicago involved computational approaches to understanding molecular structures, an area of study that bridged his background in theoretical physics with growing interest in the application of computational methods to problems in chemistry and biology. The University of Chicago's interdisciplinary research environment helped shape Jumper's approach to scientific inquiry, which would later be characterized by a willingness to move across disciplinary boundaries and apply novel computational techniques to biological questions.

In May 2025, Jumper returned to the University of Chicago to deliver the Bloch Lecture, discussing the development of AlphaFold and its implications for the protein structure prediction field.

Career

Joining DeepMind and the Protein Folding Problem

After completing his doctorate at the University of Chicago in 2017, Jumper joined DeepMind (later renamed Google DeepMind following its closer integration with Google's AI efforts), the London-based artificial intelligence research laboratory co-founded by Demis Hassabis. At DeepMind, Jumper became involved in one of the company's most ambitious scientific undertakings: applying deep learning to the protein folding problem.

The protein folding problem — the challenge of predicting the three-dimensional structure a protein will adopt based solely on its amino acid sequence — had been one of the grand challenges in molecular biology for more than five decades. The three-dimensional shape of a protein determines its function, and understanding protein structures is critical for drug design, understanding disease mechanisms, and a wide range of biological research. Traditional experimental methods for determining protein structures, such as X-ray crystallography, nuclear magnetic resonance spectroscopy, and cryo-electron microscopy, are time-consuming and expensive, often requiring months or years of work per protein. By the time Jumper joined DeepMind, computational approaches to protein structure prediction had been attempted for decades, but none had achieved the accuracy necessary to be broadly useful in practice.

Development of AlphaFold

Jumper led the team at DeepMind that developed AlphaFold, an AI-based system that uses deep learning to predict protein structures from amino acid sequences. The first version of AlphaFold was entered into the Critical Assessment of protein Structure Prediction (CASP) competition in 2018, where it outperformed other computational methods. CASP is a biennial, community-wide experiment that has served since 1994 as the benchmark for evaluating protein structure prediction methods. AlphaFold's strong performance at CASP13 in 2018 signaled that deep learning approaches had the potential to make significant advances in the field.

However, it was the second iteration of the system, AlphaFold 2, that represented a transformative breakthrough. Presented at CASP14 in November 2020, AlphaFold 2 achieved a level of accuracy in predicting protein structures that was broadly comparable to experimental methods. The system's performance was so far ahead of competing approaches that it was widely described by structural biologists as having effectively solved the protein structure prediction problem for single protein chains. AlphaFold 2 incorporated a novel neural network architecture, including attention-based mechanisms and a structure module that directly predicted atomic coordinates, representing a significant departure from earlier computational approaches to the problem.

AlphaFold's Impact on Science

Following the success at CASP14, Jumper and his colleagues at DeepMind, in collaboration with the European Bioinformatics Institute, made the AlphaFold system and its predictions freely available to the scientific community. The AlphaFold Protein Structure Database was launched, initially containing predicted structures for the human proteome and the proteomes of several model organisms. The database was subsequently expanded to include predicted structures for over 200 million proteins — covering nearly every known protein sequence — making it one of the most significant open scientific resources released in recent years.

The availability of these predicted structures has had a broad impact across biological and medical research. Biochemists and structural biologists have incorporated AlphaFold predictions into their workflows for drug design, understanding disease mechanisms, enzyme engineering, and evolutionary biology. As Fortune reported in 2025, five years after AlphaFold's debut, the system demonstrated why science represents a particularly impactful application area for artificial intelligence. While many industries continued to search for compelling applications of AI technology, the field of structural biology had already found a transformative one in protein folding.

Jumper has continued to lead research at Google DeepMind focused on extending AlphaFold's capabilities and exploring the broader application of AI methods to scientific problems. In a 2025 interview with MIT Technology Review, Jumper discussed the future directions for AlphaFold and expressed his view that large language models (LLMs) would increasingly affect scientific research, stating, "I'll be shocked if we don't see more and more LLM impact on science." This comment reflects Jumper's broader perspective on the role of AI in accelerating scientific discovery, a theme he has returned to in multiple public appearances since receiving the Nobel Prize.

Continued Work at Google DeepMind

As of 2025, Jumper continues to work at Google DeepMind, where he holds the position of senior research scientist. His ongoing research has focused on expanding the capabilities of protein structure prediction models, including predicting protein-protein interactions, protein-ligand binding, and the structures of protein complexes. The development of AlphaFold 3, which extended the system's capabilities to model interactions between proteins and other biomolecules such as DNA, RNA, and small molecules, represented a further step in this direction.

Jumper's work at DeepMind has been carried out in close collaboration with Demis Hassabis, who has provided strategic leadership for the company's scientific AI programs. Together, Hassabis and Jumper have been recognized as the principal figures behind the AlphaFold project, with Hassabis contributing the broader vision for applying AI to scientific problems and Jumper providing deep technical leadership in the development and training of the models.

Personal Life

Jumper maintains a relatively private personal life. He is based in London, United Kingdom, where Google DeepMind is headquartered. During his Nobel Week interview in December 2024, Jumper spoke about his personal motivations and the intellectual curiosity that drove his scientific career, but details about his private life remain limited in publicly available sources.

Recognition

2024 Nobel Prize in Chemistry

On October 9, 2024, the Royal Swedish Academy of Sciences announced that Jumper had been awarded a share of the 2024 Nobel Prize in Chemistry. He shared one half of the prize with Demis Hassabis "for protein structure prediction," while the other half was awarded to David Baker "for computational protein design." At the time of the award, Jumper was affiliated with Google DeepMind in London.

The Nobel Committee recognized Jumper and Hassabis for their development of AlphaFold, which the committee described as an AI model capable of predicting the three-dimensional structures of proteins from their amino acid sequences. The award reflected the scientific community's assessment that AlphaFold had made a fundamental contribution to understanding protein structure and, by extension, to biology and medicine more broadly.

Born in 1985, Jumper was 39 years old at the time of the announcement, making him one of the younger recipients of the Nobel Prize in Chemistry in recent decades. The award also reflected the growing recognition of computational and AI-based methods in traditional scientific disciplines.

The University of Chicago celebrated Jumper's achievement, noting his degrees from the institution (SM'12, PhD'17) and his role in creating the AlphaFold system. Vanderbilt University likewise acknowledged Jumper as one of its alumni, highlighting his 2007 Bachelor of Science degree in Mathematics and Physics.

Other Recognition

In 2025, Jumper and Hassabis were named to the STAT News STATUS List, which recognizes individuals who have made significant contributions to health, medicine, and the life sciences. The listing described Hassabis and Jumper as "the developers behind AlphaFold, an algorithmic model that can predict the 3D shape of proteins from their amino acid sequences."

Jumper has been invited to deliver lectures and presentations at leading academic institutions and conferences. In May 2025, he returned to the University of Chicago to deliver the Bloch Lecture, during which he discussed the development of AlphaFold and the broader implications of AI for protein science.

Legacy

Jumper's primary contribution to science — the development of AlphaFold — has had a measurable and ongoing impact on the field of structural biology and on the broader application of artificial intelligence to scientific research. The protein folding problem had been identified as one of the fundamental challenges in biology for over fifty years, and the ability to predict protein structures computationally, at a level of accuracy comparable to experimental methods, represents one of the notable scientific achievements of the early 21st century.

The AlphaFold Protein Structure Database, containing predicted structures for over 200 million proteins, has been described as one of the most significant open scientific resources ever created. Researchers in fields ranging from drug discovery to agricultural science have made use of AlphaFold predictions, and the system has been incorporated into the standard toolkit of structural biology laboratories worldwide.

Beyond its direct scientific applications, AlphaFold has served as a demonstration case for the potential of artificial intelligence in scientific research. As Jumper noted in his 2025 interview with MIT Technology Review, the intersection of AI and science is likely to deepen in the coming years, with large language models and other AI systems playing an increasing role in scientific discovery. The success of AlphaFold has contributed to increased investment in AI for science initiatives at technology companies, universities, and government agencies.

Jumper's career trajectory — from undergraduate studies in mathematics and physics, through doctoral work in computational chemistry, to leading one of the most impactful AI research projects in recent memory — illustrates the growing importance of interdisciplinary training in modern scientific research. His Nobel Prize, awarded at the age of 39, also represents one of the notable instances in which a relatively young scientist has been recognized at the highest level for work conducted in an emerging field at the intersection of computer science and the natural sciences.