AlphaFold (Japanese Wikipedia)

Analysis of information sources in references of the Wikipedia article "AlphaFold" in Japanese language version.

Last modified: 15:14:07, 8 August 2026 (UTC)

Ref.Un. Ref.Website

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1710predictioncenter.org

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118doi.org

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105deepmind.com

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94technologyreview.com

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83twitter.com

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74nature.com

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73drive.google.com

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71sciencemag.org

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64worldcat.org

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66nih.gov

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54fortune.com

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42economist.com

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33moalquraishi.wordpress.com

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32theguardian.com

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32bloomberg.com

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22newscientist.com

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11fabianfuchsml.github.io

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11nottingham.ac.uk

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11web.archive.org

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11medium.com

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11wired.com

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adsabs.harvard.edu (Global: 14^th place; Japanese: 60^th place)

ui.adsabs.harvard.edu

Jumper, John; Evans, Richard; Pritzel, Alexander; Green, Tim; Figurnov, Michael; Ronneberger, Olaf; Tunyasuvunakool, Kathryn; Bates, Russ et al. (2021-07-15). “Highly accurate protein structure prediction with AlphaFold” (英語). Nature 596 (7873): 583–589. Bibcode:2021Natur.596..583J. doi:10.1038/s41586-021-03819-2. PMC 8371605. PMID 34265844.

arxiv.org (Global: 49^th place; Japanese: 115^th place)

The structure module is stated to use a "3-d equivariant transformer architecture" (John Jumper et al. (1 December 2020), AlphaFold2 presentation, slide 12). One design for a transformer network with SE(3)-equivariance was proposed in Fabian Fuchs et al SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks, NeurIPS 2020; also website. It is not known how similar this may or may not be to what was used in AlphaFold. See also the blog post by AlQuaraishi on this.

biorxiv.org (Global: 6,055^th place; Japanese: 4,986^th place)

Jie Hou et al (2019), Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13, Proteins: Structure, Function, and Bioinformatics, 87(12) 1165-1178 doi:10.1002/prot.25697

bloomberg.com (Global: 118^th place; Japanese: 448^th place)

“DeepMind Breakthrough Helps to Solve How Diseases Invade Cells”. Bloomberg.com (英語). 2020年11月30日. 2020年11月30日閲覧.
“DeepMind Breakthrough Helps to Solve How Diseases Invade Cells”. Bloomberg.com (英語). 2020年11月30日. 2020年11月30日閲覧.

cnbc.com (Global: 303^rd place; Japanese: 886^th place)

Shead, Sam (2020年11月30日). “DeepMind solves 50-year-old 'grand challenge' with protein folding A.I.”. CNBC (英語). 2020年11月30日閲覧.

deepmind.com (Global: low place; Japanese: low place)

“AlphaFold”. Deepmind. 2020年11月30日閲覧。
“AlphaFold: Using AI for scientific discovery”. Deepmind. 2020年11月30日閲覧。
“AlphaFold: a solution to a 50-year-old grand challenge in biology”. Deepmind. 2020年11月30日閲覧。
“AlphaFold: Using AI for scientific discovery”. Deepmind. 2020年11月30日閲覧。
“Computational predictions of protein structures associated with COVID-19”. Deepmind. 2020年12月1日閲覧。

doi.org (Global: 2^nd place; Japanese: 3^rd place)

Callaway, Ewen (2020-11-30). “'It will change everything': DeepMind's AI makes gigantic leap in solving protein structures” (英語). Nature. doi:10.1038/d41586-020-03348-4.
Jumper, John; Evans, Richard; Pritzel, Alexander; Green, Tim; Figurnov, Michael; Ronneberger, Olaf; Tunyasuvunakool, Kathryn; Bates, Russ et al. (2021-07-15). “Highly accurate protein structure prediction with AlphaFold” (英語). Nature 596 (7873): 583–589. Bibcode:2021Natur.596..583J. doi:10.1038/s41586-021-03819-2. PMC 8371605. PMID 34265844.
Mohammed AlQuraishi (May 2019), AlphaFold at CASP13, Bioinformatics, 35(22), 4862–4865 doi:10.1093/bioinformatics/btz422. See also Mohammed AlQuraishi (December 9, 2018), AlphaFold @ CASP13: “What just happened?” (blog post).
Mohammed AlQuraishi (15 January 2020), A watershed moment for protein structure prediction, Nature 577, 627-628 doi:10.1038/d41586-019-03951-0
Torrisi, Mirko et al. (22 Jan. 2020), Deep learning methods in protein structure prediction. Computational and Structural Biotechnology Journal vol. 18 1301-1310. doi:10.1016/j.csbj.2019.12.011 (CC-BY-4.0)
Singh, Arunima (2020). “Deep learning 3D structures” (英語). Nature Methods 17 (3): 249. doi:10.1038/s41592-020-0779-y. ISSN 1548-7105. PMID 32132733.
Wei Zheng et al,Deep-learning contact-map guided protein structure prediction in CASP13, Proteins: Structure, Function, and Bioinformatics, 87(12) 1149-1164 doi:10.1002/prot.25792; and slides
Jie Hou et al (2019), Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13, Proteins: Structure, Function, and Bioinformatics, 87(12) 1165-1178 doi:10.1002/prot.25697
Callaway, Ewen (2020-11-30). “'It will change everything': DeepMind's AI makes gigantic leap in solving protein structures” (英語). Nature. doi:10.1038/d41586-020-03348-4.

drive.google.com (Global: 900^th place; Japanese: 2,028^th place)

Andriy Kryshtafovych (30 November 2020), Experimentalists: Are models useful? CASP 14 presentation. See also CASP 14 video stream day 1 part 1, from 0:34:30
John Moult (30 November 2020), CASP 14 introductory presentation, slide 19. See also CASP 14 video stream day 1 part 1, from 00:22:46
Lisa Kinch et al, CASP14 Tertiary Structure Prediction Assessment:Topology (FM) Category (CASP 14 presentation), slide 11. See also CASP 14 video stream day 1 part 3, from 0:18:25

economist.com (Global: 331^st place; Japanese: 1,087^th place)

“DeepMind is answering one of biology's biggest challenges”. The Economist. 2020年11月30日. ISSN 0013-0613. 2020年11月30日閲覧.
“DeepMind is answering one of biology's biggest challenges”. The Economist. 2020年11月30日. ISSN 0013-0613. 2020年11月30日閲覧.

elpais.com (Global: 89^th place; Japanese: 1,204^th place)

Nuño Dominguez, La inteligencia artificial arrasa en uno de los problemas más importantes de la biología (Artificial intelligence takes out one of the most important problems in biology), El País, 2 December 2020

fabianfuchsml.github.io (Global: low place; Japanese: low place)

The structure module is stated to use a "3-d equivariant transformer architecture" (John Jumper et al. (1 December 2020), AlphaFold2 presentation, slide 12). One design for a transformer network with SE(3)-equivariance was proposed in Fabian Fuchs et al SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks, NeurIPS 2020; also website. It is not known how similar this may or may not be to what was used in AlphaFold. See also the blog post by AlQuaraishi on this.

fold.it (Global: low place; Japanese: low place)

AlphaFold: Machine learning for protein structure prediction, Foldit, 31 January 2020

fortune.com (Global: 647^th place; Japanese: 1,590^th place)

Jeremy Kahn, Lessons from DeepMind’s breakthrough in protein-folding A.I., Fortune, 1 December 2020
Jeremy Kahn, In a major scientific breakthrough, A.I. predicts the exact shape of proteins, Fortune, 30 November 2020
Jeremy Kahn, Lessons from DeepMind's breakthrough in protein-folding A.I., Fortune, 1 December 2020
“How DeepMind's new protein-folding A.I. is already helping to combat the coronavirus pandemic.” (英語). Fortune. 2020年12月1日閲覧。

github.com (Global: 380^th place; Japanese: 404^th place)

“deepmind/deepmind-research” (英語). GitHub. 2020年11月30日閲覧。

hms.harvard.edu (Global: low place; Japanese: low place)

ccsp.hms.harvard.edu

Mohammed AlQuraishi (May 2019), AlphaFold at CASP13, Bioinformatics, 35(22), 4862–4865 doi:10.1093/bioinformatics/btz422. See also Mohammed AlQuraishi (December 9, 2018), AlphaFold @ CASP13: “What just happened?” (blog post).
Mohammed AlQuraishi (15 January 2020), A watershed moment for protein structure prediction, Nature 577, 627-628 doi:10.1038/d41586-019-03951-0

medium.com (Global: 651^st place; Japanese: 1,429^th place)

timjph.medium.com

Tim Hubbard, The secret of life, part 2: the solution of the protein folding problem., medium.com, 30 November 2020

moalquraishi.wordpress.com (Global: low place; Japanese: low place)

AlQuraishi, Mohammed (2020年12月8日). “AlphaFold2 @ CASP14: “It feels like one’s child has left home.”. The Method” (英語). Some Thoughts on a Mysterious Universe. 2020年12月15日閲覧。
Mohammed AlQuraishi (May 2019), AlphaFold at CASP13, Bioinformatics, 35(22), 4862–4865 doi:10.1093/bioinformatics/btz422. See also Mohammed AlQuraishi (December 9, 2018), AlphaFold @ CASP13: “What just happened?” (blog post).
Mohammed AlQuraishi (15 January 2020), A watershed moment for protein structure prediction, Nature 577, 627-628 doi:10.1038/d41586-019-03951-0
The structure module is stated to use a "3-d equivariant transformer architecture" (John Jumper et al. (1 December 2020), AlphaFold2 presentation, slide 12). One design for a transformer network with SE(3)-equivariance was proposed in Fabian Fuchs et al SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks, NeurIPS 2020; also website. It is not known how similar this may or may not be to what was used in AlphaFold. See also the blog post by AlQuaraishi on this.

nature.com (Global: 207^th place; Japanese: 332^nd place)

Callaway, Ewen (2020-11-30). “'It will change everything': DeepMind's AI makes gigantic leap in solving protein structures” (英語). Nature. doi:10.1038/d41586-020-03348-4.
Mohammed AlQuraishi (May 2019), AlphaFold at CASP13, Bioinformatics, 35(22), 4862–4865 doi:10.1093/bioinformatics/btz422. See also Mohammed AlQuraishi (December 9, 2018), AlphaFold @ CASP13: “What just happened?” (blog post).
Mohammed AlQuraishi (15 January 2020), A watershed moment for protein structure prediction, Nature 577, 627-628 doi:10.1038/d41586-019-03951-0
Singh, Arunima (2020). “Deep learning 3D structures” (英語). Nature Methods 17 (3): 249. doi:10.1038/s41592-020-0779-y. ISSN 1548-7105. PMID 32132733.
Callaway, Ewen (2020-11-30). “'It will change everything': DeepMind's AI makes gigantic leap in solving protein structures” (英語). Nature. doi:10.1038/d41586-020-03348-4.

newscientist.com (Global: 1,041^st place; Japanese: 1,571^st place)

Michael Le Page, DeepMind's AI biologist can decipher secrets of the machinery of life, New Scientist, 30 November 2020
The predictions of DeepMind’s latest AI could revolutionise medicine, New Scientist, 2 December 2020

nih.gov (Global: 5^th place; Japanese: 11^th place)

pubmed.ncbi.nlm.nih.gov

Jumper, John; Evans, Richard; Pritzel, Alexander; Green, Tim; Figurnov, Michael; Ronneberger, Olaf; Tunyasuvunakool, Kathryn; Bates, Russ et al. (2021-07-15). “Highly accurate protein structure prediction with AlphaFold” (英語). Nature 596 (7873): 583–589. Bibcode:2021Natur.596..583J. doi:10.1038/s41586-021-03819-2. PMC 8371605. PMID 34265844.
Singh, Arunima (2020). “Deep learning 3D structures” (英語). Nature Methods 17 (3): 249. doi:10.1038/s41592-020-0779-y. ISSN 1548-7105. PMID 32132733.
Wei Zheng et al,Deep-learning contact-map guided protein structure prediction in CASP13, Proteins: Structure, Function, and Bioinformatics, 87(12) 1149-1164 doi:10.1002/prot.25792; and slides

pmc.ncbi.nlm.nih.gov

Jumper, John; Evans, Richard; Pritzel, Alexander; Green, Tim; Figurnov, Michael; Ronneberger, Olaf; Tunyasuvunakool, Kathryn; Bates, Russ et al. (2021-07-15). “Highly accurate protein structure prediction with AlphaFold” (英語). Nature 596 (7873): 583–589. Bibcode:2021Natur.596..583J. doi:10.1038/s41586-021-03819-2. PMC 8371605. PMID 34265844.
Torrisi, Mirko et al. (22 Jan. 2020), Deep learning methods in protein structure prediction. Computational and Structural Biotechnology Journal vol. 18 1301-1310. doi:10.1016/j.csbj.2019.12.011 (CC-BY-4.0)

ncbi.nlm.nih.gov

Jumper, John; Evans, Richard; Pritzel, Alexander; Green, Tim; Figurnov, Michael; Ronneberger, Olaf; Tunyasuvunakool, Kathryn; Bates, Russ et al. (2021-07-15). “Highly accurate protein structure prediction with AlphaFold” (英語). Nature 596 (7873): 583–589. Bibcode:2021Natur.596..583J. doi:10.1038/s41586-021-03819-2. PMC 8371605. PMID 34265844.

nottingham.ac.uk (Global: 3,505^th place; Japanese: low place)

blogs.nottingham.ac.uk

Brigitte Nerlich, Protein folding and science communication: Between hype and humility, University of Nottingham blog, 4 December 2020

nytimes.com (Global: 7^th place; Japanese: 51^st place)

Cade Metz, London A.I. Lab Claims Breakthrough That Could Accelerate Drug Discovery, New York Times, 30 November 2020

occamstypewriter.org (Global: low place; Japanese: low place)

Stephen Curry, No, DeepMind has not solved protein folding, Reciprocal Space (blog), 2 December 2020

predictioncenter.org (Global: low place; Japanese: low place)

See block diagram. Also John Jumper et al. (1 December 2020), AlphaFold2 presentation, slide 10
The structure module is stated to use a "3-d equivariant transformer architecture" (John Jumper et al. (1 December 2020), AlphaFold2 presentation, slide 12). One design for a transformer network with SE(3)-equivariance was proposed in Fabian Fuchs et al SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks, NeurIPS 2020; also website. It is not known how similar this may or may not be to what was used in AlphaFold. See also the blog post by AlQuaraishi on this.
John Jumper et al. (1 December 2020), AlphaFold2 presentation, slides 12 to 20
Group performance based on combined z-scores, CASP 13, December 2018. (AlphaFold = Team 043: A7D)
See CASP 13 data tables for 043 A7D, 322 Zhang, and 089 MULTICOM
Wei Zheng et al,Deep-learning contact-map guided protein structure prediction in CASP13, Proteins: Structure, Function, and Bioinformatics, 87(12) 1149-1164 doi:10.1002/prot.25792; and slides
Andriy Kryshtafovych (30 November 2020), Experimentalists: Are models useful? CASP 14 presentation. See also CASP 14 video stream day 1 part 1, from 0:34:30
John Moult (30 November 2020), CASP 14 introductory presentation, slide 19. See also CASP 14 video stream day 1 part 1, from 00:22:46
Lisa Kinch et al, CASP14 Tertiary Structure Prediction Assessment:Topology (FM) Category (CASP 14 presentation), slide 11. See also CASP 14 video stream day 1 part 3, from 0:18:25
Artificial intelligence solution to a 50-year-old science challenge could ‘revolutionise’ medical research (press release), CASP organising committee, 30 November 2020

sciencemag.org (Global: 1,019^th place; Japanese: 1,189^th place)

Robert F. Service, ‘The game has changed.’ AI triumphs at solving protein structures, Science, 30 November 2020

spectator.co.uk (Global: 2,174^th place; Japanese: 7,454^th place)

Bissan Al-Lazikani, The solving of a biological mystery, The Spectator, 1 December 2020

spiegel.de (Global: 87^th place; Japanese: 984^th place)

Julia Merlot, Forscher hoffen auf Durchbruch für die Medikamentenforschung (Researchers hope for a breakthrough for drug research), Der Spiegel, 2 December 2020

technologyreview.com (Global: 2,515^th place; Japanese: 3,171^st place)

“DeepMind's protein-folding AI has solved a 50-year-old grand challenge of biology” (英語). MIT Technology Review. 2020年11月30日閲覧。
“DeepMind's protein-folding AI has solved a 50-year-old grand challenge of biology” (英語). MIT Technology Review. 2020年11月30日閲覧。
“DeepMind's protein-folding AI has solved a 50-year-old grand challenge of biology” (英語). MIT Technology Review. 2020年11月30日閲覧。
“DeepMind's protein-folding AI has solved a 50-year-old grand challenge of biology” (英語). MIT Technology Review. 2020年11月30日閲覧。

telegraph.co.uk (Global: 40^th place; Japanese: 195^th place)

Lizzie Roberts, 'Once in a generation advance' as Google AI researchers crack 50-year-old biological challenge. Daily Telegraph, 30 November 2020

theguardian.com (Global: 16^th place; Japanese: 81^st place)

Sample, Ian (2 December 2018). “Google's DeepMind predicts 3D shapes of proteins”. The Guardian. 2020年11月30日閲覧.
Ian Sample,DeepMind AI cracks 50-year-old problem of protein folding, The Guardian, 30 November 2020

thetimes.co.uk (Global: 1,280^th place; Japanese: 1,659^th place)

Tom Whipple, Deepmind finds biology’s ‘holy grail’ with answer to protein problem, The Times (online), 30 November 2020. In all science editor Tom Whipple wrote six articles on the subject for The Times on the day the news broke. (thread).

twitter.com (Global: 43^rd place; Japanese: 18^th place)

Mohammed AlQuraishi, CASP14 scores just came out and they’re astounding, twitter, 30 November 2020.
Demis Hassabis, "Brief update on some exciting progress on #AlphaFold!" (tweet), via twitter, 18 June 2021
Tom Whipple, "Deepmind computer solves new puzzle: life", The Times, 1 December 2020. front page image, via Twitter.

web.archive.org (Global: 1^st place; Japanese: 1^st place)

Tom Whipple, Deepmind finds biology’s ‘holy grail’ with answer to protein problem, The Times (online), 30 November 2020. In all science editor Tom Whipple wrote six articles on the subject for The Times on the day the news broke. (thread).

wired.com (Global: 254^th place; Japanese: 577^th place)

“AI Can Help Scientists Find a Covid-19 Vaccine”. Wired (アメリカ英語). ISSN 1059-1028. 2020年12月1日閲覧.

worldcat.org (Global: 4^th place; Japanese: 12^th place)

search.worldcat.org

“DeepMind is answering one of biology's biggest challenges”. The Economist. 2020年11月30日. ISSN 0013-0613. 2020年11月30日閲覧.
Singh, Arunima (2020). “Deep learning 3D structures” (英語). Nature Methods 17 (3): 249. doi:10.1038/s41592-020-0779-y. ISSN 1548-7105. PMID 32132733.
“DeepMind is answering one of biology's biggest challenges”. The Economist. 2020年11月30日. ISSN 0013-0613. 2020年11月30日閲覧.
“AI Can Help Scientists Find a Covid-19 Vaccine”. Wired (アメリカ英語). ISSN 1059-1028. 2020年12月1日閲覧.

AlphaFold (Japanese Wikipedia)

adsabs.harvard.edu (Global: 14th place; Japanese: 60th place)

ui.adsabs.harvard.edu

arxiv.org (Global: 49th place; Japanese: 115th place)

biorxiv.org (Global: 6,055th place; Japanese: 4,986th place)

bloomberg.com (Global: 118th place; Japanese: 448th place)

cnbc.com (Global: 303rd place; Japanese: 886th place)

deepmind.com (Global: low place; Japanese: low place)

doi.org (Global: 2nd place; Japanese: 3rd place)

drive.google.com (Global: 900th place; Japanese: 2,028th place)

economist.com (Global: 331st place; Japanese: 1,087th place)

elpais.com (Global: 89th place; Japanese: 1,204th place)

fabianfuchsml.github.io (Global: low place; Japanese: low place)

fold.it (Global: low place; Japanese: low place)

fortune.com (Global: 647th place; Japanese: 1,590th place)

github.com (Global: 380th place; Japanese: 404th place)

hms.harvard.edu (Global: low place; Japanese: low place)

ccsp.hms.harvard.edu

medium.com (Global: 651st place; Japanese: 1,429th place)

timjph.medium.com

moalquraishi.wordpress.com (Global: low place; Japanese: low place)

nature.com (Global: 207th place; Japanese: 332nd place)

newscientist.com (Global: 1,041st place; Japanese: 1,571st place)

nih.gov (Global: 5th place; Japanese: 11th place)

pubmed.ncbi.nlm.nih.gov

pmc.ncbi.nlm.nih.gov

ncbi.nlm.nih.gov

nottingham.ac.uk (Global: 3,505th place; Japanese: low place)

blogs.nottingham.ac.uk

nytimes.com (Global: 7th place; Japanese: 51st place)

occamstypewriter.org (Global: low place; Japanese: low place)

predictioncenter.org (Global: low place; Japanese: low place)

sciencemag.org (Global: 1,019th place; Japanese: 1,189th place)

spectator.co.uk (Global: 2,174th place; Japanese: 7,454th place)

spiegel.de (Global: 87th place; Japanese: 984th place)

technologyreview.com (Global: 2,515th place; Japanese: 3,171st place)

telegraph.co.uk (Global: 40th place; Japanese: 195th place)

theguardian.com (Global: 16th place; Japanese: 81st place)

thetimes.co.uk (Global: 1,280th place; Japanese: 1,659th place)

twitter.com (Global: 43rd place; Japanese: 18th place)

web.archive.org (Global: 1st place; Japanese: 1st place)

wired.com (Global: 254th place; Japanese: 577th place)

worldcat.org (Global: 4th place; Japanese: 12th place)

search.worldcat.org

adsabs.harvard.edu (Global: 14^th place; Japanese: 60^th place)

arxiv.org (Global: 49^th place; Japanese: 115^th place)

biorxiv.org (Global: 6,055^th place; Japanese: 4,986^th place)

bloomberg.com (Global: 118^th place; Japanese: 448^th place)

cnbc.com (Global: 303^rd place; Japanese: 886^th place)

doi.org (Global: 2^nd place; Japanese: 3^rd place)

drive.google.com (Global: 900^th place; Japanese: 2,028^th place)

economist.com (Global: 331^st place; Japanese: 1,087^th place)

elpais.com (Global: 89^th place; Japanese: 1,204^th place)

fortune.com (Global: 647^th place; Japanese: 1,590^th place)

github.com (Global: 380^th place; Japanese: 404^th place)

medium.com (Global: 651^st place; Japanese: 1,429^th place)

nature.com (Global: 207^th place; Japanese: 332^nd place)

newscientist.com (Global: 1,041^st place; Japanese: 1,571^st place)

nih.gov (Global: 5^th place; Japanese: 11^th place)

nottingham.ac.uk (Global: 3,505^th place; Japanese: low place)

nytimes.com (Global: 7^th place; Japanese: 51^st place)

sciencemag.org (Global: 1,019^th place; Japanese: 1,189^th place)

spectator.co.uk (Global: 2,174^th place; Japanese: 7,454^th place)

spiegel.de (Global: 87^th place; Japanese: 984^th place)

technologyreview.com (Global: 2,515^th place; Japanese: 3,171^st place)

telegraph.co.uk (Global: 40^th place; Japanese: 195^th place)

theguardian.com (Global: 16^th place; Japanese: 81^st place)

thetimes.co.uk (Global: 1,280^th place; Japanese: 1,659^th place)

twitter.com (Global: 43^rd place; Japanese: 18^th place)

web.archive.org (Global: 1^st place; Japanese: 1^st place)

wired.com (Global: 254^th place; Japanese: 577^th place)

worldcat.org (Global: 4^th place; Japanese: 12^th place)