John Michael Jumper is an American senior research scientist at DeepMind Technologies, London, UK, working on the application of artificial intelligence to scientific problems. He is best known for his work on AlphaFold, an AI model that predicts protein structures from their amino acid sequences with high accuracy.
Jumper received his Bachelor of Science in Physics and Mathematics from Vanderbilt University, USA, in 2007, graduating summa cum laude. He then studied at the University of Cambridge on a Marshall Scholarship, researching adaptive time-step methods for quantum Monte Carlo. He also holds an MPhil in Theoretical Condensed Matter Physics from Cambridge.
From 2008 to 2011, Jumper worked as a scientific associate at D. E. Shaw Research, where he performed basic science research using molecular dynamics computer simulations. He developed a novel clustering algorithm to extract key dynamical states from noisy observables in molecular simulations and studied the glass transition of supercooled liquids.
In 2011, Jumper began his PhD in Theoretical Chemistry at the University of Chicago, graduating in 2017. His thesis, supervised by Tobin Sosnick and Karl Freed, focused on "New methods using rigorous machine learning for coarse-grained protein folding and dynamics". During his PhD, he also worked as a software developer for high-performance computing applications.
Jumper joined DeepMind as a senior research scientist in 2018. He is part of Dr Alex Holehouse's lab, where he develops state-of-the-art methods in AI to tackle scientific challenges.
Jumper is the co-creator of AlphaFold, a deep learning algorithm that predicts protein structures with high accuracy. AlphaFold was the first machine learning algorithm to accurately predict the 3D structure of proteins, winning the Critical Assessment of Structure Prediction (CASP) competition in 2020.
AlphaFold has had a significant impact on structural biology and drug discovery, providing a better understanding of protein functions and enabling faster identification of potential drug targets. The AlphaFold database contains over 300 million protein structures, which researchers can access for free, expediting experimental work.
For his work on AlphaFold, Jumper has received numerous awards, including:
Jumper has numerous publications in prestigious scientific journals, including:
John Jumper
DeepMind
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Highly accurate protein structure prediction with AlphaFold J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, … nature 596 (7873), 583-589, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=a5goOh8AAAAJ&citationforview=a5goOh8AAAAJ:M3ejUd6NZC8C
AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models M Varadi, S Anyango, M Deshpande, S Nair, C Natassia, G Yordanova, … Nucleic acids research 50 (D1), D439-D444, 2022 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=a5goOh8AAAAJ&citationforview=a5goOh8AAAAJ:L8Ckcad2t8MC
Improved protein structure prediction using potentials from deep learning AW Senior, R Evans, J Jumper, J Kirkpatrick, L Sifre, T Green, C Qin, … Nature 577 (7792), 706-710, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=a5goOh8AAAAJ&citationforview=a5goOh8AAAAJ:hqOjcs7Dif8C
Highly accurate protein structure prediction for the human proteome K Tunyasuvunakool, J Adler, Z Wu, T Green, M Zielinski, A Žídek, … Nature 596 (7873), 590-596, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=a5goOh8AAAAJ&citationforview=a5goOh8AAAAJ:qxL8FJ1GzNcC
Atomic-level characterization of the structural dynamics of proteins DE Shaw, P Maragakis, K Lindorff-Larsen, S Piana, RO Dror, … Science 330 (6002), 341-346, 2010 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=a5goOh8AAAAJ&citationforview=a5goOh8AAAAJ:0EnyYjriUFMC
Protein complex prediction with AlphaFold-Multimer R Evans, M O’Neill, A Pritzel, N Antropova, A Senior, T Green, A Žídek, … biorxiv, 2021.10. 04.463034, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=a5goOh8AAAAJ&citationforview=a5goOh8AAAAJ:9ZlFYXVOiuMC
Effective gene expression prediction from sequence by integrating long-range interactions Ž Avsec, V Agarwal, D Visentin, JR Ledsam, A Grabska-Barwinska, … Nature methods 18 (10), 1196-1203, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=a5goOh8AAAAJ&citationforview=a5goOh8AAAAJ:ULOm3_A8WrAC
Oncogenic mutations counteract intrinsic disorder in the EGFR kinase and promote receptor dimerization Y Shan, MP Eastwood, X Zhang, ET Kim, A Arkhipov, RO Dror, J Jumper, … Cell 149 (4), 860-870, 2012 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=a5goOh8AAAAJ&citationforview=a5goOh8AAAAJ:UeHWp8X0CEIC
Accurate proteome-wide missense variant effect prediction with AlphaMissense J Cheng, G Novati, J Pan, C Bycroft, A Žemgulytė, T Applebaum, A Pritzel, … Science 381 (6664), eadg7492, 2023 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=a5goOh8AAAAJ&citationforview=a5goOh8AAAAJ:iH-uZ7U-co4C
Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13) AW Senior, R Evans, J Jumper, J Kirkpatrick, L Sifre, T Green, C Qin, … Proteins: structure, function, and bioinformatics 87 (12), 1141-1148, 2019 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=a5goOh8AAAAJ&citationforview=a5goOh8AAAAJ:zYLM7Y9cAGgC
Applying and improving AlphaFold at CASP14 J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, … Proteins: Structure, Function, and Bioinformatics 89 (12), 1711-1721, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=a5goOh8AAAAJ&citationforview=a5goOh8AAAAJ:mVmsd5A6BfQC
Innovative scattering analysis shows that hydrophobic disordered proteins are expanded in water JA Riback, MA Bowman, AM Zmyslowski, CR Knoverek, JM Jumper, … Science 358 (6360), 238-241, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=a5goOh8AAAAJ&citationforview=a5goOh8AAAAJ:qjMakFHDy7sC
Accurate structure prediction of biomolecular interactions with AlphaFold 3 J Abramson, J Adler, J Dunger, R Evans, T Green, A Pritzel, … Nature, 1-3, 2024 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=a5goOh8AAAAJ&citationforview=a5goOh8AAAAJ:GnPB-g6toBAC
Highly accurate protein structure prediction with AlphaFold., 2021, 596 J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, … DOI: https://doi. org/10.1038/s41586-021-03819-2, 583-589, 0 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=a5goOh8AAAAJ&citationforview=a5goOh8AAAAJ:hMod-77fHWUC
Protein structure predictions to atomic accuracy with AlphaFold J Jumper, D Hassabis Nature methods 19 (1), 11-12, 2022 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=a5goOh8AAAAJ&citationforview=a5goOh8AAAAJ:qUcmZB5y_30C
High accuracy protein structure prediction using deep learning J Jumper, R Evans, A Pritzel, T Green, M Figurnov, K Tunyasuvunakool, … Fourteenth critical assessment of techniques for protein structure …, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=a5goOh8AAAAJ&citationforview=a5goOh8AAAAJ:YOwf2qJgpHMC
Accelerating large language model decoding with speculative sampling C Chen, S Borgeaud, G Irving, JB Lespiau, L Sifre, J Jumper arXiv preprint arXiv:2302.01318, 2023 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=a5goOh8AAAAJ&citationforview=a5goOh8AAAAJ:4JMBOYKVnBMC
De novo structure prediction with deeplearning based scoring R Evans, J Jumper, J Kirkpatrick, L Sifre, T Green, C Qin, A Zidek, … Annu Rev Biochem 77 (363-382), 6, 2018 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=a5goOh8AAAAJ&citationforview=a5goOh8AAAAJ:ufrVoPGSRksC
Loss of conformational entropy in protein folding calculated using realistic ensembles and its implications for NMR-based calculations MC Baxa, EJ Haddadian, JM Jumper, KF Freed, TR Sosnick Proceedings of the National Academy of Sciences 111 (43), 15396-15401, 2014 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=a5goOh8AAAAJ&citationforview=a5goOh8AAAAJ:YsMSGLbcyi4C
AlphaFold 2 J Jumper, R Evans, A Pritzel, T Green, M Figurnov, K Tunyasuvunakool, … Fourteenth Critical Assessment of Techniques for Protein Structure Prediction, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=a5goOh8AAAAJ&citationforview=a5goOh8AAAAJ:ZeXyd9-uunAC