AlphaFold (Chinese Wikipedia)

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

refsWebsite

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59web.archive.org

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2moalquraishi.wordpress.com

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1foldingathome.org

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arxiv.org

結構模組被指出使用「三維等變 Transformer 架構」（John Jumper 等人（2020 年 12 月 1 日），AlphaFold 2 簡報（页面存档备份，存于互联网档案馆），第 12 頁）。
一種基於 SE(3) 等變性設計的 Transformer 網絡由 Fabian Fuchs 等人提出，SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks （页面存档备份，存于互联网档案馆），NeurIPS 2020；另見其網站（页面存档备份，存于互联网档案馆）。目前尚不清楚此設計與 AlphaFold 使用的設計相似程度。
另見 AlQuaraishi 的部落格文章（页面存档备份，存于互联网档案馆）或 Fabian Fuchs 的詳細文章（页面存档备份，存于互联网档案馆）。

bloomberg.com

DeepMind Breakthrough Helps to Solve How Diseases Invade Cells. Bloomberg.com. 2020-11-30 [2020-11-30]. （原始内容存档于2022-04-05）（英语）.

blopig.com

Carlos Outeiral, CASP14: what Google DeepMind’s AlphaFold 2 really achieved, and what it means for protein folding, biology and bioinformatics （页面存档备份，存于互联网档案馆）, Oxford Protein Informatics Group. (3 December)

chemistryworld.com

Philip Ball, Behind the Screens of AlphaFold （页面存档备份，存于互联网档案馆）, Chemistry World, 9 December 2020. See also tweets （页面存档备份，存于互联网档案馆）, 1 December

cnbc.com

Shead, Sam. DeepMind solves 50-year-old 'grand challenge' with protein folding A.I.. CNBC. 2020-11-30 [2020-11-30]. （原始内容存档于2021-01-28）（英语）.

deepmind.com

AlphaFold. Deepmind. [2020-11-30]. （原始内容存档于2021-01-19）.
AlphaFold: Using AI for scientific discovery. Deepmind. [2020-11-30]. （原始内容存档于2022-03-07）.
AlphaFold: Using AI for scientific discovery. Deepmind. [2020-11-30]. （原始内容存档于2024-10-10）.
Computational predictions of protein structures associated with COVID-19. Deepmind. [2020-12-01]. （原始内容存档于2022-03-25）.

doi.org

Jumper, John; Evans, Richard; Pritzel, Alexander; Green, Tim; Figurnov, Michael; Ronneberger, Olaf; Tunyasuvunakool, Kathryn; Bates, Russ; Žídek, Augustin; Potapenko, Anna; Bridgland, Alex; Meyer, Clemens; Kohl, Simon A A; Ballard, Andrew J; Cowie, Andrew; Romera-Paredes, Bernardino; Nikolov, Stanislav; Jain, Rishub; Adler, Jonas; Back, Trevor; Petersen, Stig; Reiman, David; Clancy, Ellen; Zielinski, Michal; Steinegger, Martin; Pacholska, Michalina; Berghammer, Tamas; Bodenstein, Sebastian; Silver, David; Vinyals, Oriol; Senior, Andrew W; Kavukcuoglu, Koray; Kohli, Pushmeet; Hassabis, Demis. Highly accurate protein structure prediction with AlphaFold. Nature. 2021-07-15, 596 (7873): 583–589. PMC 8371605 . PMID 34265844. doi:10.1038/s41586-021-03819-2  （英语）.
Ken A. Dill, S. Banu Ozkan, M. Scott Shell, and Thomas R. Weikl. The Protein Folding Problem. Annual Review of Biophysics. 2008, 37: 289–316. PMC 2443096 . PMID 18573083. doi:10.1146/annurev.biophys.37.092707.153558.
Mohammed AlQuraishi（2019 年 5 月），AlphaFold at CASP13 （页面存档备份，存于互联网档案馆），Bioinformatics，35(22)，4862–4865 doi:10.1093/bioinformatics/btz422。另見 Mohammed AlQuraishi（2018 年 12 月 9 日），AlphaFold @ CASP13: "What just happened?" （页面存档备份，存于互联网档案馆）（部落格文章）。
Mohammed AlQuraishi（2020 年 1 月 15 日），A watershed moment for protein structure prediction （页面存档备份，存于互联网档案馆），Nature 577，627–628 doi:10.1038/d41586-019-03951-0
Torrisi, Mirko 等（2020 年 1 月 22 日），Deep learning methods in protein structure prediction （页面存档备份，存于互联网档案馆），Computational and Structural Biotechnology Journal，第 18 卷，1301–1310。doi:10.1016/j.csbj.2019.12.011（CC-BY-4.0）
Callaway, Ewen. What's next for AlphaFold and the AI protein-folding revolution. Nature. 2022-04-13, 604 (7905): 234–238 [2022-04-15]. doi:10.1038/d41586-022-00997-5. （原始内容存档于2022-07-26）（英语）.
Singh, Arunima. Deep learning 3D structures. Nature Methods. 2020, 17 (3): 249. ISSN 1548-7105. PMID 32132733. S2CID 212403708. doi:10.1038/s41592-020-0779-y  （英语）.
Wei Zheng et al,Deep-learning contact-map guided protein structure prediction in CASP13 （页面存档备份，存于互联网档案馆）, Proteins: Structure, Function, and Bioinformatics, 87(12) 1149–1164 doi:10.1002/prot.25792; 和幻燈片（页面存档备份，存于互联网档案馆）
Hou, Jie; Wu, Tianqi; Cao, Renzhi; Cheng, Jianlin. Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13. Proteins: Structure, Function, and Bioinformatics (Wiley). 2019-04-25, 87 (12): 1165–1178. ISSN 0887-3585. PMC 6800999 . PMID 30985027. bioRxiv 10.1101/552422 . doi:10.1002/prot.25697.
Callaway, Ewen. 'It will change everything': DeepMind's AI makes gigantic leap in solving protein structures. Nature. 2020-11-30, 588 (7837): 203–204. Bibcode:2020Natur.588..203C. PMID 33257889. doi:10.1038/d41586-020-03348-4  （英语）.
Bagdonas, Haroldas; Fogarty, Carl A.; Fadda, Elisa; Agirre, Jon. The case for post-predictional modifications in the AlphaFold Protein Structure Database. Nature Structural & Molecular Biology. 2021-10-29, 28 (11): 869–870 [2022-07-29]. ISSN 1545-9985. PMID 34716446. S2CID 240228913. doi:10.1038/s41594-021-00680-9. （原始内容存档于2022-06-23）（英语）.

ebi.ac.uk

alphafold.ebi.ac.uk

AlphaFold Protein Structure Database. alphafold.ebi.ac.uk. [2021-07-24]. （原始内容存档于2021-07-24）.
AlphaFold Protein Structure Database. alphafold.ebi.ac.uk. [2021-07-29]. （原始内容存档于2022-07-29）.
AlphaFold Protein Structure Database. alphafold.ebi.ac.uk. [2021-07-27]. （原始内容存档于2022-07-29）.
AlphaFold Protein Structure Database. www.alphafold.ebi.ac.uk. [2022-07-29]. （原始内容存档于2022-08-02）.

elpais.com

Nuño Dominguez, La inteligencia artificial arrasa en uno de los problemas más importantes de la biología （页面存档备份，存于互联网档案馆） (Artificial intelligence takes out one of the most important problems in biology), El País, 2 December 2020

fabianfuchsml.github.io

結構模組被指出使用「三維等變 Transformer 架構」（John Jumper 等人（2020 年 12 月 1 日），AlphaFold 2 簡報（页面存档备份，存于互联网档案馆），第 12 頁）。
一種基於 SE(3) 等變性設計的 Transformer 網絡由 Fabian Fuchs 等人提出，SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks （页面存档备份，存于互联网档案馆），NeurIPS 2020；另見其網站（页面存档备份，存于互联网档案馆）。目前尚不清楚此設計與 AlphaFold 使用的設計相似程度。
另見 AlQuaraishi 的部落格文章（页面存档备份，存于互联网档案馆）或 Fabian Fuchs 的詳細文章（页面存档备份，存于互联网档案馆）。

fold.it

AlphaFold: Machine learning for protein structure prediction （页面存档备份，存于互联网档案馆），Foldit，2020 年 1 月 31 日

foldingathome.org

e.g. Greg Bowman, Protein folding and related problems remain unsolved despite AlphaFold's advance （页面存档备份，存于互联网档案馆）, Folding@home blog, 8 December 2020

fortune.com

Jeremy Kahn，Lessons from DeepMind's breakthrough in protein-folding A.I. （页面存档备份，存于互联网档案馆），《Fortune》雜誌，2020 年 12 月 1 日
Jeremy Kahn, In a major scientific breakthrough, A.I. predicts the exact shape of proteins （页面存档备份，存于互联网档案馆）, Fortune, 30 November 2020

ftchinese.com

DeepMind宣布能够预测蛋白质结构. 金融時報中文網. [2020-12-03]. （原始内容存档于2020-12-22）.

github.com

GitHub - deepmind/alphafold: Open source code for AlphaFold.. GitHub. [2021-07-24]. （原始内容存档于2021-07-23）（英语）.
deepmind/deepmind-research. GitHub. [2020-11-30]. （原始内容存档于2022-02-01）（英语）.

harvard.edu

ccsp.hms.harvard.edu

Mohammed AlQuraishi（2019 年 5 月），AlphaFold at CASP13 （页面存档备份，存于互联网档案馆），Bioinformatics，35(22)，4862–4865 doi:10.1093/bioinformatics/btz422。另見 Mohammed AlQuraishi（2018 年 12 月 9 日），AlphaFold @ CASP13: "What just happened?" （页面存档备份，存于互联网档案馆）（部落格文章）。
Mohammed AlQuraishi（2020 年 1 月 15 日），A watershed moment for protein structure prediction （页面存档备份，存于互联网档案馆），Nature 577，627–628 doi:10.1038/d41586-019-03951-0

ui.adsabs.harvard.edu

Callaway, Ewen. 'It will change everything': DeepMind's AI makes gigantic leap in solving protein structures. Nature. 2020-11-30, 588 (7837): 203–204. Bibcode:2020Natur.588..203C. PMID 33257889. doi:10.1038/d41586-020-03348-4  （英语）.

lavanguardia.com

Cristina Sáez, El último avance fundamental de la biología se basa en la investigación de un científico español （页面存档备份，存于互联网档案馆）, La Vanguardia, 2 December 2020. (Alfonso Valencia overall view)

medium.com

timjph.medium.com

Tim Hubbard, The secret of life, part 2: the solution of the protein folding problem. （页面存档备份，存于互联网档案馆）, medium.com, 30 November 2020

structural-genomics-consortium.medium.com

Aled Edwards, The AlphaFold2 success: It took a village （页面存档备份，存于互联网档案馆）, via medium.com, 5 December 2020

moalquraishi.wordpress.com

Mohammed AlQuraishi（2019 年 5 月），AlphaFold at CASP13 （页面存档备份，存于互联网档案馆），Bioinformatics，35(22)，4862–4865 doi:10.1093/bioinformatics/btz422。另見 Mohammed AlQuraishi（2018 年 12 月 9 日），AlphaFold @ CASP13: "What just happened?" （页面存档备份，存于互联网档案馆）（部落格文章）。
Mohammed AlQuraishi（2020 年 1 月 15 日），A watershed moment for protein structure prediction （页面存档备份，存于互联网档案馆），Nature 577，627–628 doi:10.1038/d41586-019-03951-0
結構模組被指出使用「三維等變 Transformer 架構」（John Jumper 等人（2020 年 12 月 1 日），AlphaFold 2 簡報（页面存档备份，存于互联网档案馆），第 12 頁）。
一種基於 SE(3) 等變性設計的 Transformer 網絡由 Fabian Fuchs 等人提出，SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks （页面存档备份，存于互联网档案馆），NeurIPS 2020；另見其網站（页面存档备份，存于互联网档案馆）。目前尚不清楚此設計與 AlphaFold 使用的設計相似程度。
另見 AlQuaraishi 的部落格文章（页面存档备份，存于互联网档案馆）或 Fabian Fuchs 的詳細文章（页面存档备份，存于互联网档案馆）。

nature.com

Mohammed AlQuraishi（2019 年 5 月），AlphaFold at CASP13 （页面存档备份，存于互联网档案馆），Bioinformatics，35(22)，4862–4865 doi:10.1093/bioinformatics/btz422。另見 Mohammed AlQuraishi（2018 年 12 月 9 日），AlphaFold @ CASP13: "What just happened?" （页面存档备份，存于互联网档案馆）（部落格文章）。
Mohammed AlQuraishi（2020 年 1 月 15 日），A watershed moment for protein structure prediction （页面存档备份，存于互联网档案馆），Nature 577，627–628 doi:10.1038/d41586-019-03951-0
Callaway, Ewen. What's next for AlphaFold and the AI protein-folding revolution. Nature. 2022-04-13, 604 (7905): 234–238 [2022-04-15]. doi:10.1038/d41586-022-00997-5. （原始内容存档于2022-07-26）（英语）.
Bagdonas, Haroldas; Fogarty, Carl A.; Fadda, Elisa; Agirre, Jon. The case for post-predictional modifications in the AlphaFold Protein Structure Database. Nature Structural & Molecular Biology. 2021-10-29, 28 (11): 869–870 [2022-07-29]. ISSN 1545-9985. PMID 34716446. S2CID 240228913. doi:10.1038/s41594-021-00680-9. （原始内容存档于2022-06-23）（英语）.

newscientist.com

Michael Le Page, DeepMind's AI biologist can decipher secrets of the machinery of life （页面存档备份，存于互联网档案馆）, New Scientist, 30 November 2020
The predictions of DeepMind’s latest AI could revolutionise medicine （页面存档备份，存于互联网档案馆）, New Scientist, 2 December 2020

nih.gov

ncbi.nlm.nih.gov

Jumper, John; Evans, Richard; Pritzel, Alexander; Green, Tim; Figurnov, Michael; Ronneberger, Olaf; Tunyasuvunakool, Kathryn; Bates, Russ; Žídek, Augustin; Potapenko, Anna; Bridgland, Alex; Meyer, Clemens; Kohl, Simon A A; Ballard, Andrew J; Cowie, Andrew; Romera-Paredes, Bernardino; Nikolov, Stanislav; Jain, Rishub; Adler, Jonas; Back, Trevor; Petersen, Stig; Reiman, David; Clancy, Ellen; Zielinski, Michal; Steinegger, Martin; Pacholska, Michalina; Berghammer, Tamas; Bodenstein, Sebastian; Silver, David; Vinyals, Oriol; Senior, Andrew W; Kavukcuoglu, Koray; Kohli, Pushmeet; Hassabis, Demis. Highly accurate protein structure prediction with AlphaFold. Nature. 2021-07-15, 596 (7873): 583–589. PMC 8371605 . PMID 34265844. doi:10.1038/s41586-021-03819-2  （英语）.
Ken A. Dill, S. Banu Ozkan, M. Scott Shell, and Thomas R. Weikl. The Protein Folding Problem. Annual Review of Biophysics. 2008, 37: 289–316. PMC 2443096 . PMID 18573083. doi:10.1146/annurev.biophys.37.092707.153558.
Torrisi, Mirko 等（2020 年 1 月 22 日），Deep learning methods in protein structure prediction （页面存档备份，存于互联网档案馆），Computational and Structural Biotechnology Journal，第 18 卷，1301–1310。doi:10.1016/j.csbj.2019.12.011（CC-BY-4.0）
Singh, Arunima. Deep learning 3D structures. Nature Methods. 2020, 17 (3): 249. ISSN 1548-7105. PMID 32132733. S2CID 212403708. doi:10.1038/s41592-020-0779-y  （英语）.
Hou, Jie; Wu, Tianqi; Cao, Renzhi; Cheng, Jianlin. Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13. Proteins: Structure, Function, and Bioinformatics (Wiley). 2019-04-25, 87 (12): 1165–1178. ISSN 0887-3585. PMC 6800999 . PMID 30985027. bioRxiv 10.1101/552422 . doi:10.1002/prot.25697.
Callaway, Ewen. 'It will change everything': DeepMind's AI makes gigantic leap in solving protein structures. Nature. 2020-11-30, 588 (7837): 203–204. Bibcode:2020Natur.588..203C. PMID 33257889. doi:10.1038/d41586-020-03348-4  （英语）.
Bagdonas, Haroldas; Fogarty, Carl A.; Fadda, Elisa; Agirre, Jon. The case for post-predictional modifications in the AlphaFold Protein Structure Database. Nature Structural & Molecular Biology. 2021-10-29, 28 (11): 869–870 [2022-07-29]. ISSN 1545-9985. PMID 34716446. S2CID 240228913. doi:10.1038/s41594-021-00680-9. （原始内容存档于2022-06-23）（英语）.

pubmed.ncbi.nlm.nih.gov

Wei Zheng et al,Deep-learning contact-map guided protein structure prediction in CASP13 （页面存档备份，存于互联网档案馆）, Proteins: Structure, Function, and Bioinformatics, 87(12) 1149–1164 doi:10.1002/prot.25792; 和幻燈片（页面存档备份，存于互联网档案馆）

nottingham.ac.uk

blogs.nottingham.ac.uk

Brigitte Nerlich, Protein folding and science communication: Between hype and humility （页面存档备份，存于互联网档案馆）, University of Nottingham blog, 4 December 2020

nytimes.com

Cade Metz, London A.I. Lab Claims Breakthrough That Could Accelerate Drug Discovery （页面存档备份，存于互联网档案馆）, New York Times, 30 November 2020

occamstypewriter.org

Stephen Curry, No, DeepMind has not solved protein folding （页面存档备份，存于互联网档案馆）, Reciprocal Space (blog), 2 December 2020

predictioncenter.org

參見方塊圖。亦見 John Jumper 等人（2020 年 12 月 1 日），AlphaFold 2 簡報（页面存档备份，存于互联网档案馆），第 10 頁
結構模組被指出使用「三維等變 Transformer 架構」（John Jumper 等人（2020 年 12 月 1 日），AlphaFold 2 簡報（页面存档备份，存于互联网档案馆），第 12 頁）。
一種基於 SE(3) 等變性設計的 Transformer 網絡由 Fabian Fuchs 等人提出，SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks （页面存档备份，存于互联网档案馆），NeurIPS 2020；另見其網站（页面存档备份，存于互联网档案馆）。目前尚不清楚此設計與 AlphaFold 使用的設計相似程度。
另見 AlQuaraishi 的部落格文章（页面存档备份，存于互联网档案馆）或 Fabian Fuchs 的詳細文章（页面存档备份，存于互联网档案馆）。
John Jumper 等人（2020 年 12 月 1 日），AlphaFold 2 簡報（页面存档备份，存于互联网档案馆），第 12 至 20 頁
根據綜合z-score的團隊表現（页面存档备份，存于互联网档案馆）, CASP 13, 2018年12月. (AlphaFold = Team 043: A7D)
見CASP 13數據表（页面存档备份，存于互联网档案馆） 043 A7D, 322 Zhang, 和 089 MULTICOM
Wei Zheng et al,Deep-learning contact-map guided protein structure prediction in CASP13 （页面存档备份，存于互联网档案馆）, Proteins: Structure, Function, and Bioinformatics, 87(12) 1149–1164 doi:10.1002/prot.25792; 和幻燈片（页面存档备份，存于互联网档案馆）
Artificial intelligence solution to a 50-year-old science challenge could ‘revolutionise’ medical research （页面存档备份，存于互联网档案馆） (press release), CASP organising committee, 30 November 2020
John Jumper et al. (1 December 2020), AlphaFold 2 （页面存档备份，存于互联网档案馆）. Presentation given at CASP 14.

rsb.org.uk

thebiologist.rsb.org.uk

Tom Ireland, How will AlphaFold change bioscience research? （页面存档备份，存于互联网档案馆）, The Biologist, 4 December 2020

science.org

Robert F. Service, 'The game has changed.' AI triumphs at solving protein structures （页面存档备份，存于互联网档案馆）, Science, 30 November 2020
Derek Lowe, In the Pipeline: What’s Crucial And What Isn’t （页面存档备份，存于互联网档案馆）, Science Translational Medicine, 25 September 2019
Derek Lowe, In the Pipeline: The Big Problems （页面存档备份，存于互联网档案馆）, Science Translational Medicine, 1 December 2020

semanticscholar.org

api.semanticscholar.org

Singh, Arunima. Deep learning 3D structures. Nature Methods. 2020, 17 (3): 249. ISSN 1548-7105. PMID 32132733. S2CID 212403708. doi:10.1038/s41592-020-0779-y  （英语）.
Bagdonas, Haroldas; Fogarty, Carl A.; Fadda, Elisa; Agirre, Jon. The case for post-predictional modifications in the AlphaFold Protein Structure Database. Nature Structural & Molecular Biology. 2021-10-29, 28 (11): 869–870 [2022-07-29]. ISSN 1545-9985. PMID 34716446. S2CID 240228913. doi:10.1038/s41594-021-00680-9. （原始内容存档于2022-06-23）（英语）.

skeptic.org.uk

David Briggs, If Google’s Alphafold2 really has solved the protein folding problem, they need to show their working （页面存档备份，存于互联网档案馆）, The Skeptic, 4 December 2020

skynettoday.com

Zero Gravitas and Jacky Liang, DeepMind’s AlphaFold 2—An Impressive Advance With Hyperbolic Coverage （页面存档备份，存于互联网档案馆）, Skynet today (blog), Stanford, 9 December 2020

sohu.com

DeepMind称AI能精确预测蛋白折叠将加速药物设计. 第一財經.

spectator.co.uk

Bissan Al-Lazikani, The solving of a biological mystery （页面存档备份，存于互联网档案馆）, The Spectator, 1 December 2020

spiegel.de

Julia Merlot, Forscher hoffen auf Durchbruch für die Medikamentenforschung （页面存档备份，存于互联网档案馆） (Researchers hope for a breakthrough for drug research), Der Spiegel, 2 December 2020
Christian Stöcker, Google greift nach dem Leben selbst （页面存档备份，存于互联网档案馆） (Google is reaching for life itself), Der Spiegel, 6 December 2020

technologyreview.com

DeepMind's protein-folding AI has solved a 50-year-old grand challenge of biology. MIT Technology Review. [2020-11-30]. （原始内容存档于2021-08-28）（英语）.

telegraph.co.uk

Lizzie Roberts, 'Once in a generation advance' as Google AI researchers crack 50-year-old biological challenge （页面存档备份，存于互联网档案馆）. Daily Telegraph, 30 November 2020

theguardian.com

Sample, Ian. Google's DeepMind predicts 3D shapes of proteins. The Guardian. 2018-12-02 [2020-11-30]. （原始内容存档于2019-07-18）.
Ian Sample,DeepMind AI cracks 50-year-old problem of protein folding （页面存档备份，存于互联网档案馆）, The Guardian, 30 November 2020
The Guardian view on DeepMind’s brain: the shape of things to come （页面存档备份，存于互联网档案馆）, The Guardian, 6 December 2020

thetimes.co.uk

Tom Whipple, Deepmind finds biology’s ‘holy grail’ with answer to protein problem （页面存档备份，存于互联网档案馆）, 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 （页面存档备份，存于互联网档案馆）).

twitter.com

Tom Whipple, "Deepmind computer solves new puzzle: life", The Times, 1 December 2020. front page image （页面存档备份，存于互联网档案馆）, via Twitter.
Tom Whipple, Deepmind finds biology’s ‘holy grail’ with answer to protein problem （页面存档备份，存于互联网档案馆）, 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 （页面存档备份，存于互联网档案馆）).
Demis Hassabis, "Brief update on some exciting progress on #AlphaFold!" （页面存档备份，存于互联网档案馆） (tweet), via twitter, 18 June 2021
Philip Ball, Behind the Screens of AlphaFold （页面存档备份，存于互联网档案馆）, Chemistry World, 9 December 2020. See also tweets （页面存档备份，存于互联网档案馆）, 1 December

web.archive.org

AlphaFold. Deepmind. [2020-11-30]. （原始内容存档于2021-01-19）.
DeepMind's protein-folding AI has solved a 50-year-old grand challenge of biology. MIT Technology Review. [2020-11-30]. （原始内容存档于2021-08-28）（英语）.
DeepMind宣布能够预测蛋白质结构. 金融時報中文網. [2020-12-03]. （原始内容存档于2020-12-22）.
Shead, Sam. DeepMind solves 50-year-old 'grand challenge' with protein folding A.I.. CNBC. 2020-11-30 [2020-11-30]. （原始内容存档于2021-01-28）（英语）.
“阿尔法折叠”精准预测蛋白质三维结构. 科技日报. [2020-12-03]. （原始内容存档于2020-12-05）.
GitHub - deepmind/alphafold: Open source code for AlphaFold.. GitHub. [2021-07-24]. （原始内容存档于2021-07-23）（英语）.
AlphaFold Protein Structure Database. alphafold.ebi.ac.uk. [2021-07-24]. （原始内容存档于2021-07-24）.
AlphaFold: Using AI for scientific discovery. Deepmind. [2020-11-30]. （原始内容存档于2022-03-07）.
Robert F. Service, 'The game has changed.' AI triumphs at solving protein structures （页面存档备份，存于互联网档案馆）, Science, 30 November 2020
Mohammed AlQuraishi（2019 年 5 月），AlphaFold at CASP13 （页面存档备份，存于互联网档案馆），Bioinformatics，35(22)，4862–4865 doi:10.1093/bioinformatics/btz422。另見 Mohammed AlQuraishi（2018 年 12 月 9 日），AlphaFold @ CASP13: "What just happened?" （页面存档备份，存于互联网档案馆）（部落格文章）。
Mohammed AlQuraishi（2020 年 1 月 15 日），A watershed moment for protein structure prediction （页面存档备份，存于互联网档案馆），Nature 577，627–628 doi:10.1038/d41586-019-03951-0
AlphaFold: Machine learning for protein structure prediction （页面存档备份，存于互联网档案馆），Foldit，2020 年 1 月 31 日
Torrisi, Mirko 等（2020 年 1 月 22 日），Deep learning methods in protein structure prediction （页面存档备份，存于互联网档案馆），Computational and Structural Biotechnology Journal，第 18 卷，1301–1310。doi:10.1016/j.csbj.2019.12.011（CC-BY-4.0）
Jeremy Kahn，Lessons from DeepMind's breakthrough in protein-folding A.I. （页面存档备份，存于互联网档案馆），《Fortune》雜誌，2020 年 12 月 1 日
參見方塊圖。亦見 John Jumper 等人（2020 年 12 月 1 日），AlphaFold 2 簡報（页面存档备份，存于互联网档案馆），第 10 頁
結構模組被指出使用「三維等變 Transformer 架構」（John Jumper 等人（2020 年 12 月 1 日），AlphaFold 2 簡報（页面存档备份，存于互联网档案馆），第 12 頁）。
一種基於 SE(3) 等變性設計的 Transformer 網絡由 Fabian Fuchs 等人提出，SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks （页面存档备份，存于互联网档案馆），NeurIPS 2020；另見其網站（页面存档备份，存于互联网档案馆）。目前尚不清楚此設計與 AlphaFold 使用的設計相似程度。
另見 AlQuaraishi 的部落格文章（页面存档备份，存于互联网档案馆）或 Fabian Fuchs 的詳細文章（页面存档备份，存于互联网档案馆）。
John Jumper 等人（2020 年 12 月 1 日），AlphaFold 2 簡報（页面存档备份，存于互联网档案馆），第 12 至 20 頁
Callaway, Ewen. What's next for AlphaFold and the AI protein-folding revolution. Nature. 2022-04-13, 604 (7905): 234–238 [2022-04-15]. doi:10.1038/d41586-022-00997-5. （原始内容存档于2022-07-26）（英语）.
根據綜合z-score的團隊表現（页面存档备份，存于互联网档案馆）, CASP 13, 2018年12月. (AlphaFold = Team 043: A7D)
Sample, Ian. Google's DeepMind predicts 3D shapes of proteins. The Guardian. 2018-12-02 [2020-11-30]. （原始内容存档于2019-07-18）.
AlphaFold: Using AI for scientific discovery. Deepmind. [2020-11-30]. （原始内容存档于2024-10-10）.
見CASP 13數據表（页面存档备份，存于互联网档案馆） 043 A7D, 322 Zhang, 和 089 MULTICOM
Wei Zheng et al,Deep-learning contact-map guided protein structure prediction in CASP13 （页面存档备份，存于互联网档案馆）, Proteins: Structure, Function, and Bioinformatics, 87(12) 1149–1164 doi:10.1002/prot.25792; 和幻燈片（页面存档备份，存于互联网档案馆）
DeepMind Breakthrough Helps to Solve How Diseases Invade Cells. Bloomberg.com. 2020-11-30 [2020-11-30]. （原始内容存档于2022-04-05）（英语）.
deepmind/deepmind-research. GitHub. [2020-11-30]. （原始内容存档于2022-02-01）（英语）.
Artificial intelligence solution to a 50-year-old science challenge could ‘revolutionise’ medical research （页面存档备份，存于互联网档案馆） (press release), CASP organising committee, 30 November 2020
Brigitte Nerlich, Protein folding and science communication: Between hype and humility （页面存档备份，存于互联网档案馆）, University of Nottingham blog, 4 December 2020
Michael Le Page, DeepMind's AI biologist can decipher secrets of the machinery of life （页面存档备份，存于互联网档案馆）, New Scientist, 30 November 2020
The predictions of DeepMind’s latest AI could revolutionise medicine （页面存档备份，存于互联网档案馆）, New Scientist, 2 December 2020
Cade Metz, London A.I. Lab Claims Breakthrough That Could Accelerate Drug Discovery （页面存档备份，存于互联网档案馆）, New York Times, 30 November 2020
Ian Sample,DeepMind AI cracks 50-year-old problem of protein folding （页面存档备份，存于互联网档案馆）, The Guardian, 30 November 2020
Lizzie Roberts, 'Once in a generation advance' as Google AI researchers crack 50-year-old biological challenge （页面存档备份，存于互联网档案馆）. Daily Telegraph, 30 November 2020
Nuño Dominguez, La inteligencia artificial arrasa en uno de los problemas más importantes de la biología （页面存档备份，存于互联网档案馆） (Artificial intelligence takes out one of the most important problems in biology), El País, 2 December 2020
Jeremy Kahn, In a major scientific breakthrough, A.I. predicts the exact shape of proteins （页面存档备份，存于互联网档案馆）, Fortune, 30 November 2020
Julia Merlot, Forscher hoffen auf Durchbruch für die Medikamentenforschung （页面存档备份，存于互联网档案馆） (Researchers hope for a breakthrough for drug research), Der Spiegel, 2 December 2020
Bissan Al-Lazikani, The solving of a biological mystery （页面存档备份，存于互联网档案馆）, The Spectator, 1 December 2020
Tom Whipple, "Deepmind computer solves new puzzle: life", The Times, 1 December 2020. front page image （页面存档备份，存于互联网档案馆）, via Twitter.
Tom Whipple, Deepmind finds biology’s ‘holy grail’ with answer to protein problem （页面存档备份，存于互联网档案馆）, 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 （页面存档备份，存于互联网档案馆）).
Tim Hubbard, The secret of life, part 2: the solution of the protein folding problem. （页面存档备份，存于互联网档案馆）, medium.com, 30 November 2020
Christian Stöcker, Google greift nach dem Leben selbst （页面存档备份，存于互联网档案馆） (Google is reaching for life itself), Der Spiegel, 6 December 2020
John Jumper et al. (1 December 2020), AlphaFold 2 （页面存档备份，存于互联网档案馆）. Presentation given at CASP 14.
Carlos Outeiral, CASP14: what Google DeepMind’s AlphaFold 2 really achieved, and what it means for protein folding, biology and bioinformatics （页面存档备份，存于互联网档案馆）, Oxford Protein Informatics Group. (3 December)
Aled Edwards, The AlphaFold2 success: It took a village （页面存档备份，存于互联网档案馆）, via medium.com, 5 December 2020
David Briggs, If Google’s Alphafold2 really has solved the protein folding problem, they need to show their working （页面存档备份，存于互联网档案馆）, The Skeptic, 4 December 2020
The Guardian view on DeepMind’s brain: the shape of things to come （页面存档备份，存于互联网档案馆）, The Guardian, 6 December 2020
Demis Hassabis, "Brief update on some exciting progress on #AlphaFold!" （页面存档备份，存于互联网档案馆） (tweet), via twitter, 18 June 2021
Tom Ireland, How will AlphaFold change bioscience research? （页面存档备份，存于互联网档案馆）, The Biologist, 4 December 2020
Stephen Curry, No, DeepMind has not solved protein folding （页面存档备份，存于互联网档案馆）, Reciprocal Space (blog), 2 December 2020
Derek Lowe, In the Pipeline: What’s Crucial And What Isn’t （页面存档备份，存于互联网档案馆）, Science Translational Medicine, 25 September 2019
Philip Ball, Behind the Screens of AlphaFold （页面存档备份，存于互联网档案馆）, Chemistry World, 9 December 2020. See also tweets （页面存档备份，存于互联网档案馆）, 1 December
Derek Lowe, In the Pipeline: The Big Problems （页面存档备份，存于互联网档案馆）, Science Translational Medicine, 1 December 2020
Bagdonas, Haroldas; Fogarty, Carl A.; Fadda, Elisa; Agirre, Jon. The case for post-predictional modifications in the AlphaFold Protein Structure Database. Nature Structural & Molecular Biology. 2021-10-29, 28 (11): 869–870 [2022-07-29]. ISSN 1545-9985. PMID 34716446. S2CID 240228913. doi:10.1038/s41594-021-00680-9. （原始内容存档于2022-06-23）（英语）.
e.g. Greg Bowman, Protein folding and related problems remain unsolved despite AlphaFold's advance （页面存档备份，存于互联网档案馆）, Folding@home blog, 8 December 2020
Cristina Sáez, El último avance fundamental de la biología se basa en la investigación de un científico español （页面存档备份，存于互联网档案馆）, La Vanguardia, 2 December 2020. (Alfonso Valencia overall view)
Zero Gravitas and Jacky Liang, DeepMind’s AlphaFold 2—An Impressive Advance With Hyperbolic Coverage （页面存档备份，存于互联网档案馆）, Skynet today (blog), Stanford, 9 December 2020
AlphaFold Protein Structure Database. alphafold.ebi.ac.uk. [2021-07-29]. （原始内容存档于2022-07-29）.
AlphaFold Protein Structure Database. alphafold.ebi.ac.uk. [2021-07-27]. （原始内容存档于2022-07-29）.
AlphaFold Protein Structure Database. www.alphafold.ebi.ac.uk. [2022-07-29]. （原始内容存档于2022-08-02）.
AI Can Help Scientists Find a Covid-19 Vaccine. Wired. [2020-12-01]. ISSN 1059-1028. （原始内容存档于2022-04-23）（美国英语）.
Computational predictions of protein structures associated with COVID-19. Deepmind. [2020-12-01]. （原始内容存档于2022-03-25）.

wired.com

AI Can Help Scientists Find a Covid-19 Vaccine. Wired. [2020-12-01]. ISSN 1059-1028. （原始内容存档于2022-04-23）（美国英语）.

worldcat.org

Singh, Arunima. Deep learning 3D structures. Nature Methods. 2020, 17 (3): 249. ISSN 1548-7105. PMID 32132733. S2CID 212403708. doi:10.1038/s41592-020-0779-y  （英语）.
Hou, Jie; Wu, Tianqi; Cao, Renzhi; Cheng, Jianlin. Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13. Proteins: Structure, Function, and Bioinformatics (Wiley). 2019-04-25, 87 (12): 1165–1178. ISSN 0887-3585. PMC 6800999 . PMID 30985027. bioRxiv 10.1101/552422 . doi:10.1002/prot.25697.
Bagdonas, Haroldas; Fogarty, Carl A.; Fadda, Elisa; Agirre, Jon. The case for post-predictional modifications in the AlphaFold Protein Structure Database. Nature Structural & Molecular Biology. 2021-10-29, 28 (11): 869–870 [2022-07-29]. ISSN 1545-9985. PMID 34716446. S2CID 240228913. doi:10.1038/s41594-021-00680-9. （原始内容存档于2022-06-23）（英语）.
AI Can Help Scientists Find a Covid-19 Vaccine. Wired. [2020-12-01]. ISSN 1059-1028. （原始内容存档于2022-04-23）（美国英语）.

xinhuanet.com

“阿尔法折叠”精准预测蛋白质三维结构. 科技日报. [2020-12-03]. （原始内容存档于2020-12-05）.