John M. Jumper

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For other people named John Jumper, see John Jumper (disambiguation).
John Jumper
Born
John Michael Jumper
Alma mater
Known forAlphaFold
AwardsBreakthrough Prize in Life Sciences (2022)
Nature's 10 (2021)
Scientific career
FieldsArtificial intelligence
Machine learning
InstitutionsGoogle
Deepmind
ThesisNew methods using rigorous machine learning for coarse-grained protein folding and dynamics (2017)
Doctoral advisorKarl Freed[1]

John Michael Jumper is a senior research scientist at DeepMind Technologies.[4][5][6] Jumper and his colleagues created AlphaFold,[7] an artificial intelligence (AI) model to predict protein structures from their amino acid sequence with high accuracy.[8] Jumper has stated that the AlphaFold team plans to release 100 million protein structures.[9] The scientific journal Nature included Jumper as one of the ten "people who mattered" in science in their annual listing of Nature's 10 in 2021.[8][3]

Education[edit]

Jumper was educated at the University of Chicago where he was awarded a PhD in 2017 for research on using machine learning to predict protein folding supervised by Karl Freed.[1] Jumper also holds a Master of Philosophy (MPhil) degree in Physics from the University of Cambridge and a Bachelor of Science degree in Physics and Mathematics from Vanderbilt University.[2]

Career and research[edit]

Jumper's research investigates algorithms for protein structure prediction.[4]

AlphaFold[edit]

this image represents the final product of AlphaFold and it compares its results with other competitors at the CASP competition

AlphaFold[7][10] is a deep learning algorithm developed by Jumper and his team at DeepMind, a research lab acquired by Google's parent company Alphabet Inc. It is an artificial intelligence program which performs predictions of protein structure.[11]

Awards and honours[edit]

In November 2020, AlphaFold was named the winner of the Critical Assessment of Structure Prediction (CASP) competition. This international competition benchmarks algorithms to determine which one can best predict the 3D structure of proteins. AlphaFold won the competition, out performing other algorithms and making it the first machine learning algorithm to be able to accurately predict the 3D structure of proteins. Jumper and his team are now[when?] working on improving the accuracy of AlphaFold. They’re also exploring ways to use AlphaFold to develop new treatments for diseases and create new materials with unique properties. In 2021 he was awarded the BBVA Foundation Frontiers of Knowledge Award in the category "Biology and Biomedicine".[12] In 2022 Jumper received the Wiley Prize in Biomedical Sciences[13] and for 2023 the Breakthrough Prize in Life Sciences for developing AlphaFold, which accurately predicts the structure of a protein.[14] In 2023 he was awarded the Canada Gairdner International Award.[15]

References[edit]

  1. ^ a b Jumper, John Michael (2017). New methods using rigorous machine learning for coarse-grained protein folding and dynamics. chicago.edu (PhD thesis). University of Chicago. doi:10.6082/M1BZ647N. OCLC 1237239279. ProQuest 1883866286.
  2. ^ a b "John Jumper at DeepMind". falling-walls.com.
  3. ^ a b John M. Jumper on LinkedIn Edit this at Wikidata
  4. ^ a b John M. Jumper publications indexed by Google Scholar Edit this at Wikidata
  5. ^ John M. Jumper publications from Europe PubMed Central
  6. ^ Eisenstein, Michael (2021). "Artificial intelligence powers protein-folding predictions". Nature. Springer Nature. 599 (7886): 706–708. doi:10.1038/d41586-021-03499-y. S2CID 244528561. Retrieved 24 December 2021.
  7. ^ a b John Jumper; Richard Evans; Alexander Pritzel; et al. (15 July 2021). "Highly accurate protein structure prediction with AlphaFold". Nature. Bibcode:2021Natur.596..583J. doi:10.1038/S41586-021-03819-2. ISSN 1476-4687. PMC 8371605. Wikidata Q107555821.
  8. ^ a b Maxmen, Amy (2021). "Nature's 10: John Jumper: Protein predictor". Nature. Springer Nature. 600 (7890): 591–604. doi:10.1038/d41586-021-03621-0. PMID 34912110. S2CID 245256541. Retrieved 24 December 2021.
  9. ^ Browne, Grace (2021). "DeepMind's AI has finally shown how useful it can be". wired.com. Retrieved 24 December 2021.
  10. ^ Andrew W Senior; Richard Evans; John Jumper; et al. (15 January 2020). "Improved protein structure prediction using potentials from deep learning". Nature. 577 (7792): 706–710. doi:10.1038/S41586-019-1923-7. ISSN 1476-4687. PMID 31942072. Wikidata Q92669549.
  11. ^ "AlphaFold". Deepmind. Retrieved 30 November 2020.
  12. ^ "BBVA Foundation Frontiers of Knowledge Award 2022". frontiersofknowledgeawards-fbbva.es.
  13. ^ "Wiley Prize 2022". wiley.com.
  14. ^ "Breakthrough Prizes 2023". breakthroughprize.org. Retrieved 22 September 2022.
  15. ^ Canada Gairdner International Award 2023


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