AlphaFold (English Wikipedia)

Ball, Phillip (9 December 2020). "Behind the screens of AlphaFold". Chemistry World. Archived from the original on 15 August 2021. Retrieved 10 December 2020.

cnbc.com (Global: 303^rd place; English: 191^st place)

Shead, Sam (2020-11-30). "DeepMind solves 50-year-old 'grand challenge' with protein folding A.I." CNBC. Archived from the original on 2021-01-28. Retrieved 2020-11-30.

cnn.com (Global: 36^th place; English: 26^th place)

edition.cnn.com

Hunt, Christian Edwards, Katie (9 October 2024). "Scientists who used AI to 'crack the code' of almost all proteins win Nobel Prize in chemistry". CNN. Archived from the original on 10 October 2024. Retrieved 9 October 2024.{{cite news}}: CS1 maint: multiple names: authors list (link)

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

"AlphaFold". Deepmind. Archived from the original on 19 January 2021. Retrieved 30 November 2020.
"AlphaFold: Using AI for scientific discovery". Deepmind. 15 January 2020. Archived from the original on 2022-03-07. Retrieved 2020-11-30.
"AlphaFold: Using AI for scientific discovery". Deepmind. January 2020. Archived from the original on 10 October 2024. Retrieved 30 November 2020.
"AlphaFold: a solution to a 50-year-old grand challenge in biology". Deepmind. 30 November 2020. Archived from the original on 30 November 2020. Retrieved 30 November 2020.
"Putting the power of AlphaFold into the world's hands". Deepmind. 22 July 2022. Archived from the original on 24 July 2021. Retrieved 24 July 2021.
"Computational predictions of protein structures associated with COVID-19". Deepmind. 4 August 2020. Archived from the original on 2022-03-25. Retrieved 2020-12-01.

deepmind.google (Global: low place; English: low place)

Tunyasuvunakool, Kathryn; Adler, Jonas; Wu, Zachary; Green, Tim; Zielinski, Michal; Žídek, Augustin; Bridgland, Alex; Cowie, Andrew; Meyer, Clemens; Laydon, Agata; Velanka*, Sameer; Kleywegt *, Gerard J; Bateman*, Alex; Evans, Richard; Pritzel, Alexander; Figurnov, Michael; Ronneberger, Olaf; Bates, Russ; Kohl, Simon A. A.; Potapenko, Anna; Ballard, Andrew J; Romera-Paredes, Bernardino; Nikolov, Stanislav; Jain, Rishub; Clancy, Ellen; Reiman, David; Petersen, Stig; Senior, Andrew; Kavukcuoglu, Koray; Birney *, Ewan; Kohli, Pushmeet; Jumper, John; Hassabis, Demis (2021-07-22). "Enabling high-accuracy protein structure prediction at the proteome scale". Google DeepMind. (* external authors). Retrieved 2026-08-17.

doi.org (Global: 2^nd place; English: 2^nd place)

Stoddart, Charlotte (1 March 2022). "Structural biology: How proteins got their close-up". Knowable Magazine. doi:10.1146/knowable-022822-1. S2CID 247206999. Archived from the original on 7 April 2022. Retrieved 25 March 2022.
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 (2021-07-15). "Highly accurate protein structure prediction with AlphaFold". Nature. 596 (7873): 583–589. Bibcode:2021Natur.596..583J. doi:10.1038/s41586-021-03819-2. PMC 8371605. PMID 34265844.
Callaway, Ewen (2020-11-30). "'It will change everything': DeepMind's AI makes gigantic leap in solving protein structures". Nature. 588 (7837): 203–204. Bibcode:2020Natur.588..203C. doi:10.1038/d41586-020-03348-4. PMID 33257889. S2CID 227243204.
Abramson, Josh; Adler, Jonas; Dunger, Jack; Evans, Richard; Green, Tim; Pritzel, Alexander; Ronneberger, Olaf; Willmore, Lindsay; Ballard, Andrew J.; Bambrick, Joshua; Bodenstein, Sebastian W.; Evans, David A.; Hung, Chia-Chun; O'Neill, Michael; Reiman, David (2024-05-08). "Accurate structure prediction of biomolecular interactions with AlphaFold 3". Nature. 630 (8016): 493–500. Bibcode:2024Natur.630..493A. doi:10.1038/s41586-024-07487-w. ISSN 1476-4687. PMC 11168924. PMID 38718835.
Mohammed AlQuraishi (May 2019), AlphaFold at CASP13 Archived 2021-11-22 at the Wayback Machine, Bioinformatics, 35(22), 4862–4865 doi:10.1093/bioinformatics/btz422. See also Mohammed AlQuraishi (December 9, 2018), AlphaFold @ CASP13: "What just happened?" Archived 2022-07-29 at the Wayback Machine (blog post).
Mohammed AlQuraishi (15 January 2020), A watershed moment for protein structure prediction Archived 2022-06-23 at the Wayback Machine, Nature 577, 627–628 doi:10.1038/d41586-019-03951-0
Jumper, John; et al. (August 2021). "Highly accurate protein structure prediction with AlphaFold". Nature. 596 (7873): 583–589. Bibcode:2021Natur.596..583J. doi:10.1038/s41586-021-03819-2. ISSN 1476-4687. PMC 8371605. PMID 34265844.
Callaway, Ewen (13 April 2022). "What's next for AlphaFold and the AI protein-folding revolution". Nature. 604 (7905): 234–238. Bibcode:2022Natur.604..234C. doi:10.1038/d41586-022-00997-5. PMID 35418629. S2CID 248156195.
Singh, Arunima (2020). "Deep learning 3D structures". Nature Methods. 17 (3): 249. doi:10.1038/s41592-020-0779-y. ISSN 1548-7105. PMID 32132733. S2CID 212403708.
Wei Zheng et al,Deep-learning contact-map guided protein structure prediction in CASP13 Archived 2022-01-22 at the Wayback Machine, Proteins: Structure, Function, and Bioinformatics, 87(12) 1149–1164 doi:10.1002/prot.25792; and slides Archived 2022-07-26 at the Wayback Machine
Hou, Jie; Wu, Tianqi; Cao, Renzhi; Cheng, Jianlin (2019-04-25). "Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13". Proteins: Structure, Function, and Bioinformatics. 87 (12). Wiley: 1165–1178. bioRxiv 10.1101/552422. doi:10.1002/prot.25697. ISSN 0887-3585. PMC 6800999. PMID 30985027.
Callaway, Ewen (2022-12-13). "After AlphaFold: protein-folding contest seeks next big breakthrough". Nature. 613 (7942): 13–14. doi:10.1038/d41586-022-04438-1. PMID 36513827. S2CID 254660427.
Guo, Song-Bin; Meng, Yuan; Lin, Liteng; Zhou, Zhen-Zhong; Li, Hai-Long; Tian, Xiao-Peng; Huang, Wei-Juan (2024-10-05). "Artificial intelligence alphafold model for molecular biology and drug discovery: a machine-learning-driven informatics investigation". Molecular Cancer. 23 (1): 223. doi:10.1186/s12943-024-02140-6. ISSN 1476-4598. PMC 11452995. PMID 39369244.
Cui, Haochuan; Wang, Yuzhuo; Li, Kai (2025). Beyond Citations: Tracing and Validating the Rapid Adoption of AlphaFold in Biomedical Research Through Full-Text Analysis (PDF). 20th International Conference on Scientometrics and Informetrics (ISSI 2025). Yerevan, Armenia: ISSI Society. pp. 2002–2009. doi:10.51408/issi2025_147.
Hill, Ryan; Stein, Carolyn (17 July 2026) [First version 11 March 2026]. How Artificial Intelligence Shapes Science: Evidence from AlphaFold (PDF) (Working paper) (Revised ed.). Earlier version issued as NBER Working Paper No. 35143 doi:10.3386/w35143. Retrieved 2026-07-30.
Hoelzemann, Johannes; Manso, Gustavo; Nagaraj, Abhishek; Tranchero, Matteo (May 2024). The Streetlight Effect in Data-Driven Exploration (Working paper). NBER Working Papers. Cambridge, MA: National Bureau of Economic Research. doi:10.3386/w32401.
Sun, Mengyi; Choi, Sukwoong; Yin, Yian (2026-04-07). "AI predictions and the expansion of scientific frontiers: Evidence from structural biology". bioRxiv 10.64898/2026.04.06.716821.
ByteDance AML AI4Science Team; Chen, Xinshi; Zhang, Yuxuan; Lu, Chan; Ma, Wenzhi; Guan, Jiaqi; Gong, Chengyue; Yang, Jincai; Zhang, Hanyu; Zhang, Ke; Wu, Shenghao; Zhou, Kuangqi; Yang, Yanping; Liu, Zhenyu; Wang, Lan; Shi, Bo; Shi, Shaochen; Xiao, Wenzhi (2025-01-11). "Protenix - Advancing Structure Prediction Through a Comprehensive AlphaFold3 Reproduction". bioRxiv 10.1101/2025.01.08.631967.{{cite bioRxiv}}: CS1 maint: numeric names: authors list (link)
Naddaf, Miryam (28 October 2025). "Open-source protein structure AI aims to match AlphaFold". Nature. doi:10.1038/d41586-025-03546-y. PMID 41152525.
Lin, Z; Akin, H; Rao, R; Hie, B; Zhu, Z; Lu, W; Smetanin, N; Verkuil, R; Kabeli, O; Shmueli, Y; Dos Santos Costa, A; Fazel-Zarandi, M; Sercu, T; Candido, S; Rives, A (17 March 2023). "Evolutionary-scale prediction of atomic-level protein structure with a language model". Science. 379 (6637): 1123–1130. Bibcode:2023Sci...379.1123L. doi:10.1126/science.ade2574. PMID 36927031.
Steinegger, Martin; Söding, Johannes (2017). "MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets". Nature Biotechnology. 35 (11): 1026–1028. doi:10.1038/nbt.3988. hdl:11858/00-001M-0000-002E-1967-3. ISSN 1087-0156. PMID 29035372.
Mirdita, Milot; Schütze, Konstantin; Moriwaki, Yoshitaka; Heo, Lim; Ovchinnikov, Sergey; Steinegger, Martin (2022-05-30). "ColabFold: Making protein folding accessible to all". Nature Methods. 19 (6): 679–682. doi:10.1038/s41592-022-01488-1. PMC 9184281. PMID 35637307.
Callaway, Ewen (2022-07-28). "'The entire protein universe': AI predicts shape of nearly every known protein". Nature. 608 (7921): 15–16. Bibcode:2022Natur.608...15C. doi:10.1038/d41586-022-02083-2. PMID 35902752. S2CID 251159714.
Lau, Andy M.; Bordin, Nicola; Kandathil, Shaun M.; Sillitoe, Ian; Waman, Vaishali P.; Wells, Jude; Orengo, Christine A.; Jones, David T. (November 2024). "Exploring structural diversity across the protein universe with The Encyclopedia of Domains". Science. 386 (6721) eadq4946. Bibcode:2024Sci...386q4946L. doi:10.1126/science.adq4946. PMC 7618865. PMID 39480926.
Fleming, Jennifer; Magana, Paulyna; Nair, Sreenath; Tsenkov, Maxim; Bertoni, Damian; Pidruchna, Ivanna; Lima Afonso, Marcelo Querino; Midlik, Adam; Paramval, Urmila; Žídek, Augustin; Laydon, Agata; Kovalevskiy, Oleg; Pan, Joshua; Cheng, Jun; Avsec, Žiga; Bycroft, Clare; Wong, Lai Hong; Last, Meera; Mirdita, Milot; Steinegger, Martin; Kohli, Pushmeet; Váradi, Mihály; Velankar, Sameer (August 2025). "AlphaFold Protein Structure Database and 3D-Beacons: New Data and Capabilities". Journal of Molecular Biology. 437 (15) 168967. doi:10.1016/j.jmb.2025.168967. PMID 40133787.
Hekkelman, Maarten L.; de Vries, Ida; Joosten, Robbie P.; Perrakis, Anastassis (February 2023). "AlphaFill: enriching AlphaFold models with ligands and cofactors". Nature Methods. 20 (2): 205–213. Bibcode:2023NaMet..20..205H. doi:10.1038/s41592-022-01685-y. PMC 9911346. PMID 36424442.
Rosignoli, Serena; Pacelli, Maddalena; Manganiello, Francesca; Paiardini, Alessandro (February 2025). "An outlook on structural biology after Alpha Fold: tools, limits and perspectives". FEBS Open Bio. 15 (2): 202–222. doi:10.1002/2211-5463.13902. PMC 11788754. PMID 39313455.
Špačková, Anna; Vávra, Ondřej; Raček, Tomáš; Bazgier, Václav; Sehnal, David; Damborský, Jiří; Svobodová, Radka; Bednář, David; Berka, Karel (5 January 2024). "ChannelsDB 2.0: a comprehensive database of protein tunnels and pores in AlphaFold era". Nucleic Acids Research. 52 (D1): D413–D418. doi:10.1093/nar/gkad1012. PMC 10767935. PMID 37956324.
Lang, Benjamin; Babu, M. Madan (2025). "AlphaSync is an enhanced AlphaFold structure database synchronized with UniProt". Nature Structural & Molecular Biology. 32 (12): 2628–2632. bioRxiv 10.1101/2025.03.12.642845. doi:10.1038/s41594-025-01719-x. PMID 41219581.
Schaeffer, RD; Zhang, J; Cong, Q; Grishin, NV (March 2026). "ECOD: Classification of domains in AFDB Swiss-Prot structure predictions". PLOS Computational Biology. 22 (3) e1013431. Bibcode:2026PLSCB..2213431S. doi:10.1371/journal.pcbi.1013431. PMC 13048469. PMID 41911251.
Sommer, Markus J; Cha, Sooyoung; Varabyou, Ales; Rincon, Natalia; Park, Sukhwan; Minkin, Ilia; Pertea, Mihaela; Steinegger, Martin; Salzberg, Steven L (15 December 2022). "Structure-guided isoform identification for the human transcriptome". eLife. 11 e82556. doi:10.7554/eLife.82556. PMC 9812405. PMID 36519529.
McBride, John M.; Polev, Konstantin; Abdirasulov, Amirbek; Reinharz, Vladimir; Grzybowski, Bartosz A.; Tlusty, Tsvi (2023-11-20). "AlphaFold2 Can Predict Single-Mutation Effects". Physical Review Letters. 131 (21) 218401. arXiv:2204.06860. Bibcode:2023PhRvL.131u8401M. doi:10.1103/PhysRevLett.131.218401. ISSN 0031-9007. PMID 38072605. Archived from the original on 2024-06-09. Retrieved 2023-11-26.
McBride, John M.; Tlusty, Tsvi (2024-08-26). "AI-Predicted Protein Deformation Encodes Energy Landscape Perturbation". Physical Review Letters. 133 (9) 098401. arXiv:2311.18222. Bibcode:2024PhRvL.133i8401M. doi:10.1103/PhysRevLett.133.098401. ISSN 0031-9007. PMID 39270162.
Abramson, Josh; Adler, Jonas; Dunger, Jack; Evans, Richard; Green, Tim; Pritzel, Alexander; Ronneberger, Olaf; Willmore, Lindsay; Ballard, Andrew J.; Bambrick, Joshua; Bodenstein, Sebastian W.; Evans, David A.; Hung, Chia-Chun; O'Neill, Michael; Reiman, David (June 2024). "Accurate structure prediction of biomolecular interactions with AlphaFold 3". Nature. 630 (8016): 493–500. Bibcode:2024Natur.630..493A. doi:10.1038/s41586-024-07487-w. ISSN 1476-4687. PMC 11168924. PMID 38718835.
Bagdonas, Haroldas; Fogarty, Carl A.; Fadda, Elisa; Agirre, Jon (2021-10-29). "The case for post-predictional modifications in the AlphaFold Protein Structure Database" (PDF). Nature Structural & Molecular Biology. 28 (11): 869–870. doi:10.1038/s41594-021-00680-9. ISSN 1545-9985. PMID 34716446. S2CID 240228913. Archived (PDF) from the original on 2024-01-13. Retrieved 2024-01-13.
An, Hyun Joo; Froehlich, John W; Lebrilla, Carlito B (2009-10-01). "Determination of glycosylation sites and site-specific heterogeneity in glycoproteins". Current Opinion in Chemical Biology. Analytical Techniques/Mechanisms. 13 (4): 421–426. doi:10.1016/j.cbpa.2009.07.022. ISSN 1367-5931. PMC 2749913. PMID 19700364.
Škrinjar, Peter; Eberhardt, Jérôme; Tauriello, Gerardo; Schwede, Torsten; Durairaj, Janani (2026). "Evaluating generalization in protein–ligand cofolding methods". Nature Structural & Molecular Biology. 33 (5): 782–794. bioRxiv 10.1101/2025.02.03.636309. doi:10.1038/s41594-026-01797-5. PMID 42103936.
Masters, Matthew R.; Mahmoud, Amr H.; Lill, Markus A. (2025-10-06). "Investigating whether deep learning models for co-folding learn the physics of protein-ligand interactions". Nature Communications. 16 (1): 8854. Bibcode:2025NatCo..16.8854M. doi:10.1038/s41467-025-63947-5. ISSN 2041-1723. PMC 12501370. PMID 41053181.
Dabrowski-Tumanski, Pawel; Stasiak, Andrzej (7 November 2023). "AlphaFold Blindness to Topological Barriers Affects Its Ability to Correctly Predict Proteins' Topology". Molecules. 28 (22): 7462. doi:10.3390/molecules28227462. PMC 10672856. PMID 38005184.
Aubel, Margaux; Eicholt, Lars; Bornberg-Bauer, Erich (2023-03-29). "Assessing structure and disorder prediction tools for de novo emerged proteins in the age of machine learning". F1000Research. 12: 347. doi:10.12688/f1000research.130443.1. PMC 10126731. PMID 37113259.
Michaud, Jennifer M.; Madani, Ali; Fraser, James S. (November 2022). "A language model beats alphafold2 on orphans". Nature Biotechnology. 40 (11): 1576–1577. doi:10.1038/s41587-022-01466-0. PMC 9669189. PMID 36192635.
Middendorf, Lasse; Eicholt, Lars A. (June 2024). "Random, de novo, and conserved proteins: How structure and disorder predictors perform differently". Proteins. 92 (6): 757–767. doi:10.1002/prot.26652. PMID 38226524.
Liu, Jing; Yuan, Rongqing; Shao, Wei; Wang, Jitong; Silman, Israel; Sussman, Joel L. (August 2023). "Do "Newly Born" orphan proteins resemble "Never Born" proteins? A study using three deep learning algorithms". Proteins. 91 (8): 1097–1115. doi:10.1002/prot.26496. PMID 37092778.

ebi.ac.uk (Global: 4,422^nd place; English: 7,719^th place)

alphafold.ebi.ac.uk

"AlphaFold Protein Structure Database". alphafold.ebi.ac.uk. Archived from the original on 2021-07-24. Retrieved 2021-07-24.
"AlphaFold Protein Structure Database". alphafold.ebi.ac.uk. Archived from the original on 2022-07-29. Retrieved 2021-07-29.
"AlphaFold Protein Structure Database". alphafold.ebi.ac.uk. Archived from the original on 29 July 2022. Retrieved 27 July 2021.
"AlphaFold Protein Structure Database". www.alphafold.ebi.ac.uk. Archived from the original on 2022-08-02. Retrieved 2021-07-24.
"What use cases does AlphaFold not support?". AlphaFold Protein Structure Database. Archived from the original on 2022-07-29. Retrieved 2022-05-15.

economist.com (Global: 331^st place; English: 272^nd place)

"DeepMind is answering one of biology's biggest challenges". The Economist. 2020-11-30. ISSN 0013-0613. Archived from the original on 2020-12-03. Retrieved 2020-11-30.

elpais.com (Global: 89^th place; English: 268^th place)

Domínguez, Nuño (2020-12-02). "La inteligencia artificial arrasa en uno de los problemas más importantes de la biología". El País (in Spanish). Archived from the original on 2022-07-26. Retrieved 2024-05-12.

engadget.com (Global: 613^th place; English: 442^nd place)

"Google shuts down its Nobel-prize winning AlphaFold project as it focuses on Gemini". Engadget. Retrieved 2026-07-29.

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

The structure module is stated to use a "3-d equivariant transformer architecture" (John Jumper et al. (1 December 2020), AlphaFold 2 presentation Archived 2022-07-03 at the Wayback Machine, 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 Archived 2021-10-07 at the Wayback Machine, NeurIPS 2020; also website Archived 2022-07-03 at the Wayback Machine. It is not known how similar this may or may not be to what was used in AlphaFold.
See also the blog post Archived 2020-12-08 at the Wayback Machine by AlQuaraishi on this, or the more detailed post Archived 2022-07-03 at the Wayback Machine by Fabian Fuchs

fastcompany.com (Global: 1,399^th place; English: 772^nd place)

"DeepMind's latest AI breakthrough could turbocharge drug discovery". Fast Company. ISSN 1085-9241. Archived from the original on 2023-01-24. Retrieved 2023-01-24.

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

AlphaFold: Machine learning for protein structure prediction Archived 2022-05-12 at the Wayback Machine, Foldit, 31 January 2020

foldingathome.org (Global: low place; English: low place)

e.g. Greg Bowman, Protein folding and related problems remain unsolved despite AlphaFold's advance Archived 2022-07-13 at the Wayback Machine, Folding@home blog, 8 December 2020

forbes.com (Global: 71^st place; English: 54^th place)

Knapp, Alex. "2023 Breakthrough Prizes Announced: Deepmind's Protein Folders Awarded $3 Million". Forbes. Archived from the original on 2024-05-09. Retrieved 2024-05-09.

fortune.com (Global: 647^th place; English: 421^st place)

Jeremy Kahn, Lessons from DeepMind's breakthrough in protein-folding A.I. Archived 2022-04-08 at the Wayback Machine, Fortune, 1 December 2020
"How DeepMind's new protein-folding A.I. is already helping to combat the coronavirus pandemic". Fortune. Archived from the original on 2020-11-30. Retrieved 2020-12-01.

ft.com (Global: 193^rd place; English: 127^th place)

Murgia, Madhumuti (29 July 2026). "Google DeepMind dismantles Nobel-winning AlphaFold team in strategy shift". Financial Times. Retrieved 29 July 2026.

github.com (Global: 380^th place; English: 337^th place)

"GitHub - deepmind/alphafold: Open source code for AlphaFold". GitHub. Archived from the original on 2021-07-23. Retrieved 2021-07-24.
"deepmind/deepmind-research". GitHub. Archived from the original on 2022-02-01. Retrieved 2020-11-30.
google-deepmind/alphafold3, Google DeepMind, 2025-02-08, retrieved 2025-02-08

hdl.handle.net (Global: 66^th place; English: 34^th place)

Steinegger, Martin; Söding, Johannes (2017). "MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets". Nature Biotechnology. 35 (11): 1026–1028. doi:10.1038/nbt.3988. hdl:11858/00-001M-0000-002E-1967-3. ISSN 1087-0156. PMID 29035372.

hms.harvard.edu (Global: low place; English: 7,550^th place)

ccsp.hms.harvard.edu

Mohammed AlQuraishi (May 2019), AlphaFold at CASP13 Archived 2021-11-22 at the Wayback Machine, Bioinformatics, 35(22), 4862–4865 doi:10.1093/bioinformatics/btz422. See also Mohammed AlQuraishi (December 9, 2018), AlphaFold @ CASP13: "What just happened?" Archived 2022-07-29 at the Wayback Machine (blog post).
Mohammed AlQuraishi (15 January 2020), A watershed moment for protein structure prediction Archived 2022-06-23 at the Wayback Machine, Nature 577, 627–628 doi:10.1038/d41586-019-03951-0

innovationgrowthlab.org (Global: low place; English: low place)

"AI in Science: Evidence of impact from AlphaFold 2". Innovation Growth Lab. 2025-11-24. Retrieved 2026-07-30.

knowablemagazine.org (Global: low place; English: 9,041^st place)

Stoddart, Charlotte (1 March 2022). "Structural biology: How proteins got their close-up". Knowable Magazine. doi:10.1146/knowable-022822-1. S2CID 247206999. Archived from the original on 7 April 2022. Retrieved 25 March 2022.

maynoothuniversity.ie (Global: low place; English: low place)

mural.maynoothuniversity.ie

Bagdonas, Haroldas; Fogarty, Carl A.; Fadda, Elisa; Agirre, Jon (2021-10-29). "The case for post-predictional modifications in the AlphaFold Protein Structure Database" (PDF). Nature Structural & Molecular Biology. 28 (11): 869–870. doi:10.1038/s41594-021-00680-9. ISSN 1545-9985. PMID 34716446. S2CID 240228913. Archived (PDF) from the original on 2024-01-13. Retrieved 2024-01-13.

medium.com (Global: 651^st place; English: 479^th place)

timjph.medium.com

Tim Hubbard, The secret of life, part 2: the solution of the protein folding problem. Archived 2022-05-14 at the Wayback Machine, medium.com, 30 November 2020

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

Mohammed AlQuraishi (May 2019), AlphaFold at CASP13 Archived 2021-11-22 at the Wayback Machine, Bioinformatics, 35(22), 4862–4865 doi:10.1093/bioinformatics/btz422. See also Mohammed AlQuraishi (December 9, 2018), AlphaFold @ CASP13: "What just happened?" Archived 2022-07-29 at the Wayback Machine (blog post).
Mohammed AlQuraishi (15 January 2020), A watershed moment for protein structure prediction Archived 2022-06-23 at the Wayback Machine, 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), AlphaFold 2 presentation Archived 2022-07-03 at the Wayback Machine, 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 Archived 2021-10-07 at the Wayback Machine, NeurIPS 2020; also website Archived 2022-07-03 at the Wayback Machine. It is not known how similar this may or may not be to what was used in AlphaFold.
See also the blog post Archived 2020-12-08 at the Wayback Machine by AlQuaraishi on this, or the more detailed post Archived 2022-07-03 at the Wayback Machine by Fabian Fuchs

nature.com (Global: 207^th place; English: 347^th place)

Mohammed AlQuraishi (May 2019), AlphaFold at CASP13 Archived 2021-11-22 at the Wayback Machine, Bioinformatics, 35(22), 4862–4865 doi:10.1093/bioinformatics/btz422. See also Mohammed AlQuraishi (December 9, 2018), AlphaFold @ CASP13: "What just happened?" Archived 2022-07-29 at the Wayback Machine (blog post).
Mohammed AlQuraishi (15 January 2020), A watershed moment for protein structure prediction Archived 2022-06-23 at the Wayback Machine, Nature 577, 627–628 doi:10.1038/d41586-019-03951-0
Steinegger, Martin; Söding, Johannes (2017). "MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets". Nature Biotechnology. 35 (11): 1026–1028. doi:10.1038/nbt.3988. hdl:11858/00-001M-0000-002E-1967-3. ISSN 1087-0156. PMID 29035372.
AlphaFold heralds a data-driven revolution in biology and medicine Archived 2024-10-10 at the Wayback Machine, by Janet M. Thornton, Roman A. Laskowski & Neera Borkakoti, Nature Medicine, volume 12, pages 1666–1669, 12 October 2021

nber.org (Global: 3,811^th place; English: 2,424^th place)

Hill, Ryan; Stein, Carolyn (17 July 2026) [First version 11 March 2026]. How Artificial Intelligence Shapes Science: Evidence from AlphaFold (PDF) (Working paper) (Revised ed.). Earlier version issued as NBER Working Paper No. 35143 doi:10.3386/w35143. Retrieved 2026-07-30.

newscientist.com (Global: 1,041^st place; English: 901^st place)

Michael Le Page, DeepMind's AI biologist can decipher secrets of the machinery of life Archived 2022-08-02 at the Wayback Machine, New Scientist, 30 November 2020
The predictions of DeepMind's latest AI could revolutionise medicine Archived 2021-11-07 at the Wayback Machine, New Scientist, 2 December 2020

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

pubmed.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 (2021-07-15). "Highly accurate protein structure prediction with AlphaFold". Nature. 596 (7873): 583–589. Bibcode:2021Natur.596..583J. doi:10.1038/s41586-021-03819-2. PMC 8371605. PMID 34265844.
Callaway, Ewen (2020-11-30). "'It will change everything': DeepMind's AI makes gigantic leap in solving protein structures". Nature. 588 (7837): 203–204. Bibcode:2020Natur.588..203C. doi:10.1038/d41586-020-03348-4. PMID 33257889. S2CID 227243204.
Abramson, Josh; Adler, Jonas; Dunger, Jack; Evans, Richard; Green, Tim; Pritzel, Alexander; Ronneberger, Olaf; Willmore, Lindsay; Ballard, Andrew J.; Bambrick, Joshua; Bodenstein, Sebastian W.; Evans, David A.; Hung, Chia-Chun; O'Neill, Michael; Reiman, David (2024-05-08). "Accurate structure prediction of biomolecular interactions with AlphaFold 3". Nature. 630 (8016): 493–500. Bibcode:2024Natur.630..493A. doi:10.1038/s41586-024-07487-w. ISSN 1476-4687. PMC 11168924. PMID 38718835.
Jumper, John; et al. (August 2021). "Highly accurate protein structure prediction with AlphaFold". Nature. 596 (7873): 583–589. Bibcode:2021Natur.596..583J. doi:10.1038/s41586-021-03819-2. ISSN 1476-4687. PMC 8371605. PMID 34265844.
Callaway, Ewen (13 April 2022). "What's next for AlphaFold and the AI protein-folding revolution". Nature. 604 (7905): 234–238. Bibcode:2022Natur.604..234C. doi:10.1038/d41586-022-00997-5. PMID 35418629. S2CID 248156195.
Singh, Arunima (2020). "Deep learning 3D structures". Nature Methods. 17 (3): 249. doi:10.1038/s41592-020-0779-y. ISSN 1548-7105. PMID 32132733. S2CID 212403708.
Wei Zheng et al,Deep-learning contact-map guided protein structure prediction in CASP13 Archived 2022-01-22 at the Wayback Machine, Proteins: Structure, Function, and Bioinformatics, 87(12) 1149–1164 doi:10.1002/prot.25792; and slides Archived 2022-07-26 at the Wayback Machine
Hou, Jie; Wu, Tianqi; Cao, Renzhi; Cheng, Jianlin (2019-04-25). "Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13". Proteins: Structure, Function, and Bioinformatics. 87 (12). Wiley: 1165–1178. bioRxiv 10.1101/552422. doi:10.1002/prot.25697. ISSN 0887-3585. PMC 6800999. PMID 30985027.
Callaway, Ewen (2022-12-13). "After AlphaFold: protein-folding contest seeks next big breakthrough". Nature. 613 (7942): 13–14. doi:10.1038/d41586-022-04438-1. PMID 36513827. S2CID 254660427.
Guo, Song-Bin; Meng, Yuan; Lin, Liteng; Zhou, Zhen-Zhong; Li, Hai-Long; Tian, Xiao-Peng; Huang, Wei-Juan (2024-10-05). "Artificial intelligence alphafold model for molecular biology and drug discovery: a machine-learning-driven informatics investigation". Molecular Cancer. 23 (1): 223. doi:10.1186/s12943-024-02140-6. ISSN 1476-4598. PMC 11452995. PMID 39369244.
Naddaf, Miryam (28 October 2025). "Open-source protein structure AI aims to match AlphaFold". Nature. doi:10.1038/d41586-025-03546-y. PMID 41152525.
Lin, Z; Akin, H; Rao, R; Hie, B; Zhu, Z; Lu, W; Smetanin, N; Verkuil, R; Kabeli, O; Shmueli, Y; Dos Santos Costa, A; Fazel-Zarandi, M; Sercu, T; Candido, S; Rives, A (17 March 2023). "Evolutionary-scale prediction of atomic-level protein structure with a language model". Science. 379 (6637): 1123–1130. Bibcode:2023Sci...379.1123L. doi:10.1126/science.ade2574. PMID 36927031.
Steinegger, Martin; Söding, Johannes (2017). "MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets". Nature Biotechnology. 35 (11): 1026–1028. doi:10.1038/nbt.3988. hdl:11858/00-001M-0000-002E-1967-3. ISSN 1087-0156. PMID 29035372.
Mirdita, Milot; Schütze, Konstantin; Moriwaki, Yoshitaka; Heo, Lim; Ovchinnikov, Sergey; Steinegger, Martin (2022-05-30). "ColabFold: Making protein folding accessible to all". Nature Methods. 19 (6): 679–682. doi:10.1038/s41592-022-01488-1. PMC 9184281. PMID 35637307.
Callaway, Ewen (2022-07-28). "'The entire protein universe': AI predicts shape of nearly every known protein". Nature. 608 (7921): 15–16. Bibcode:2022Natur.608...15C. doi:10.1038/d41586-022-02083-2. PMID 35902752. S2CID 251159714.
Lau, Andy M.; Bordin, Nicola; Kandathil, Shaun M.; Sillitoe, Ian; Waman, Vaishali P.; Wells, Jude; Orengo, Christine A.; Jones, David T. (November 2024). "Exploring structural diversity across the protein universe with The Encyclopedia of Domains". Science. 386 (6721) eadq4946. Bibcode:2024Sci...386q4946L. doi:10.1126/science.adq4946. PMC 7618865. PMID 39480926.
Fleming, Jennifer; Magana, Paulyna; Nair, Sreenath; Tsenkov, Maxim; Bertoni, Damian; Pidruchna, Ivanna; Lima Afonso, Marcelo Querino; Midlik, Adam; Paramval, Urmila; Žídek, Augustin; Laydon, Agata; Kovalevskiy, Oleg; Pan, Joshua; Cheng, Jun; Avsec, Žiga; Bycroft, Clare; Wong, Lai Hong; Last, Meera; Mirdita, Milot; Steinegger, Martin; Kohli, Pushmeet; Váradi, Mihály; Velankar, Sameer (August 2025). "AlphaFold Protein Structure Database and 3D-Beacons: New Data and Capabilities". Journal of Molecular Biology. 437 (15) 168967. doi:10.1016/j.jmb.2025.168967. PMID 40133787.
Hekkelman, Maarten L.; de Vries, Ida; Joosten, Robbie P.; Perrakis, Anastassis (February 2023). "AlphaFill: enriching AlphaFold models with ligands and cofactors". Nature Methods. 20 (2): 205–213. Bibcode:2023NaMet..20..205H. doi:10.1038/s41592-022-01685-y. PMC 9911346. PMID 36424442.
Rosignoli, Serena; Pacelli, Maddalena; Manganiello, Francesca; Paiardini, Alessandro (February 2025). "An outlook on structural biology after Alpha Fold: tools, limits and perspectives". FEBS Open Bio. 15 (2): 202–222. doi:10.1002/2211-5463.13902. PMC 11788754. PMID 39313455.
Špačková, Anna; Vávra, Ondřej; Raček, Tomáš; Bazgier, Václav; Sehnal, David; Damborský, Jiří; Svobodová, Radka; Bednář, David; Berka, Karel (5 January 2024). "ChannelsDB 2.0: a comprehensive database of protein tunnels and pores in AlphaFold era". Nucleic Acids Research. 52 (D1): D413–D418. doi:10.1093/nar/gkad1012. PMC 10767935. PMID 37956324.
Lang, Benjamin; Babu, M. Madan (2025). "AlphaSync is an enhanced AlphaFold structure database synchronized with UniProt". Nature Structural & Molecular Biology. 32 (12): 2628–2632. bioRxiv 10.1101/2025.03.12.642845. doi:10.1038/s41594-025-01719-x. PMID 41219581.
Schaeffer, RD; Zhang, J; Cong, Q; Grishin, NV (March 2026). "ECOD: Classification of domains in AFDB Swiss-Prot structure predictions". PLOS Computational Biology. 22 (3) e1013431. Bibcode:2026PLSCB..2213431S. doi:10.1371/journal.pcbi.1013431. PMC 13048469. PMID 41911251.
Sommer, Markus J; Cha, Sooyoung; Varabyou, Ales; Rincon, Natalia; Park, Sukhwan; Minkin, Ilia; Pertea, Mihaela; Steinegger, Martin; Salzberg, Steven L (15 December 2022). "Structure-guided isoform identification for the human transcriptome". eLife. 11 e82556. doi:10.7554/eLife.82556. PMC 9812405. PMID 36519529.
McBride, John M.; Polev, Konstantin; Abdirasulov, Amirbek; Reinharz, Vladimir; Grzybowski, Bartosz A.; Tlusty, Tsvi (2023-11-20). "AlphaFold2 Can Predict Single-Mutation Effects". Physical Review Letters. 131 (21) 218401. arXiv:2204.06860. Bibcode:2023PhRvL.131u8401M. doi:10.1103/PhysRevLett.131.218401. ISSN 0031-9007. PMID 38072605. Archived from the original on 2024-06-09. Retrieved 2023-11-26.
McBride, John M.; Tlusty, Tsvi (2024-08-26). "AI-Predicted Protein Deformation Encodes Energy Landscape Perturbation". Physical Review Letters. 133 (9) 098401. arXiv:2311.18222. Bibcode:2024PhRvL.133i8401M. doi:10.1103/PhysRevLett.133.098401. ISSN 0031-9007. PMID 39270162.
Abramson, Josh; Adler, Jonas; Dunger, Jack; Evans, Richard; Green, Tim; Pritzel, Alexander; Ronneberger, Olaf; Willmore, Lindsay; Ballard, Andrew J.; Bambrick, Joshua; Bodenstein, Sebastian W.; Evans, David A.; Hung, Chia-Chun; O'Neill, Michael; Reiman, David (June 2024). "Accurate structure prediction of biomolecular interactions with AlphaFold 3". Nature. 630 (8016): 493–500. Bibcode:2024Natur.630..493A. doi:10.1038/s41586-024-07487-w. ISSN 1476-4687. PMC 11168924. PMID 38718835.
Bagdonas, Haroldas; Fogarty, Carl A.; Fadda, Elisa; Agirre, Jon (2021-10-29). "The case for post-predictional modifications in the AlphaFold Protein Structure Database" (PDF). Nature Structural & Molecular Biology. 28 (11): 869–870. doi:10.1038/s41594-021-00680-9. ISSN 1545-9985. PMID 34716446. S2CID 240228913. Archived (PDF) from the original on 2024-01-13. Retrieved 2024-01-13.
An, Hyun Joo; Froehlich, John W; Lebrilla, Carlito B (2009-10-01). "Determination of glycosylation sites and site-specific heterogeneity in glycoproteins". Current Opinion in Chemical Biology. Analytical Techniques/Mechanisms. 13 (4): 421–426. doi:10.1016/j.cbpa.2009.07.022. ISSN 1367-5931. PMC 2749913. PMID 19700364.
Škrinjar, Peter; Eberhardt, Jérôme; Tauriello, Gerardo; Schwede, Torsten; Durairaj, Janani (2026). "Evaluating generalization in protein–ligand cofolding methods". Nature Structural & Molecular Biology. 33 (5): 782–794. bioRxiv 10.1101/2025.02.03.636309. doi:10.1038/s41594-026-01797-5. PMID 42103936.
Masters, Matthew R.; Mahmoud, Amr H.; Lill, Markus A. (2025-10-06). "Investigating whether deep learning models for co-folding learn the physics of protein-ligand interactions". Nature Communications. 16 (1): 8854. Bibcode:2025NatCo..16.8854M. doi:10.1038/s41467-025-63947-5. ISSN 2041-1723. PMC 12501370. PMID 41053181.
Dabrowski-Tumanski, Pawel; Stasiak, Andrzej (7 November 2023). "AlphaFold Blindness to Topological Barriers Affects Its Ability to Correctly Predict Proteins' Topology". Molecules. 28 (22): 7462. doi:10.3390/molecules28227462. PMC 10672856. PMID 38005184.
Aubel, Margaux; Eicholt, Lars; Bornberg-Bauer, Erich (2023-03-29). "Assessing structure and disorder prediction tools for de novo emerged proteins in the age of machine learning". F1000Research. 12: 347. doi:10.12688/f1000research.130443.1. PMC 10126731. PMID 37113259.
Michaud, Jennifer M.; Madani, Ali; Fraser, James S. (November 2022). "A language model beats alphafold2 on orphans". Nature Biotechnology. 40 (11): 1576–1577. doi:10.1038/s41587-022-01466-0. PMC 9669189. PMID 36192635.
Middendorf, Lasse; Eicholt, Lars A. (June 2024). "Random, de novo, and conserved proteins: How structure and disorder predictors perform differently". Proteins. 92 (6): 757–767. doi:10.1002/prot.26652. PMID 38226524.
Liu, Jing; Yuan, Rongqing; Shao, Wei; Wang, Jitong; Silman, Israel; Sussman, Joel L. (August 2023). "Do "Newly Born" orphan proteins resemble "Never Born" proteins? A study using three deep learning algorithms". Proteins. 91 (8): 1097–1115. doi:10.1002/prot.26496. PMID 37092778.

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 (2021-07-15). "Highly accurate protein structure prediction with AlphaFold". Nature. 596 (7873): 583–589. Bibcode:2021Natur.596..583J. doi:10.1038/s41586-021-03819-2. PMC 8371605. PMID 34265844.
Abramson, Josh; Adler, Jonas; Dunger, Jack; Evans, Richard; Green, Tim; Pritzel, Alexander; Ronneberger, Olaf; Willmore, Lindsay; Ballard, Andrew J.; Bambrick, Joshua; Bodenstein, Sebastian W.; Evans, David A.; Hung, Chia-Chun; O'Neill, Michael; Reiman, David (2024-05-08). "Accurate structure prediction of biomolecular interactions with AlphaFold 3". Nature. 630 (8016): 493–500. Bibcode:2024Natur.630..493A. doi:10.1038/s41586-024-07487-w. ISSN 1476-4687. PMC 11168924. PMID 38718835.
Jumper, John; et al. (August 2021). "Highly accurate protein structure prediction with AlphaFold". Nature. 596 (7873): 583–589. Bibcode:2021Natur.596..583J. doi:10.1038/s41586-021-03819-2. ISSN 1476-4687. PMC 8371605. PMID 34265844.
Hou, Jie; Wu, Tianqi; Cao, Renzhi; Cheng, Jianlin (2019-04-25). "Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13". Proteins: Structure, Function, and Bioinformatics. 87 (12). Wiley: 1165–1178. bioRxiv 10.1101/552422. doi:10.1002/prot.25697. ISSN 0887-3585. PMC 6800999. PMID 30985027.
Guo, Song-Bin; Meng, Yuan; Lin, Liteng; Zhou, Zhen-Zhong; Li, Hai-Long; Tian, Xiao-Peng; Huang, Wei-Juan (2024-10-05). "Artificial intelligence alphafold model for molecular biology and drug discovery: a machine-learning-driven informatics investigation". Molecular Cancer. 23 (1): 223. doi:10.1186/s12943-024-02140-6. ISSN 1476-4598. PMC 11452995. PMID 39369244.
Mirdita, Milot; Schütze, Konstantin; Moriwaki, Yoshitaka; Heo, Lim; Ovchinnikov, Sergey; Steinegger, Martin (2022-05-30). "ColabFold: Making protein folding accessible to all". Nature Methods. 19 (6): 679–682. doi:10.1038/s41592-022-01488-1. PMC 9184281. PMID 35637307.
Lau, Andy M.; Bordin, Nicola; Kandathil, Shaun M.; Sillitoe, Ian; Waman, Vaishali P.; Wells, Jude; Orengo, Christine A.; Jones, David T. (November 2024). "Exploring structural diversity across the protein universe with The Encyclopedia of Domains". Science. 386 (6721) eadq4946. Bibcode:2024Sci...386q4946L. doi:10.1126/science.adq4946. PMC 7618865. PMID 39480926.
Hekkelman, Maarten L.; de Vries, Ida; Joosten, Robbie P.; Perrakis, Anastassis (February 2023). "AlphaFill: enriching AlphaFold models with ligands and cofactors". Nature Methods. 20 (2): 205–213. Bibcode:2023NaMet..20..205H. doi:10.1038/s41592-022-01685-y. PMC 9911346. PMID 36424442.
Rosignoli, Serena; Pacelli, Maddalena; Manganiello, Francesca; Paiardini, Alessandro (February 2025). "An outlook on structural biology after Alpha Fold: tools, limits and perspectives". FEBS Open Bio. 15 (2): 202–222. doi:10.1002/2211-5463.13902. PMC 11788754. PMID 39313455.
Špačková, Anna; Vávra, Ondřej; Raček, Tomáš; Bazgier, Václav; Sehnal, David; Damborský, Jiří; Svobodová, Radka; Bednář, David; Berka, Karel (5 January 2024). "ChannelsDB 2.0: a comprehensive database of protein tunnels and pores in AlphaFold era". Nucleic Acids Research. 52 (D1): D413–D418. doi:10.1093/nar/gkad1012. PMC 10767935. PMID 37956324.
Schaeffer, RD; Zhang, J; Cong, Q; Grishin, NV (March 2026). "ECOD: Classification of domains in AFDB Swiss-Prot structure predictions". PLOS Computational Biology. 22 (3) e1013431. Bibcode:2026PLSCB..2213431S. doi:10.1371/journal.pcbi.1013431. PMC 13048469. PMID 41911251.
Sommer, Markus J; Cha, Sooyoung; Varabyou, Ales; Rincon, Natalia; Park, Sukhwan; Minkin, Ilia; Pertea, Mihaela; Steinegger, Martin; Salzberg, Steven L (15 December 2022). "Structure-guided isoform identification for the human transcriptome". eLife. 11 e82556. doi:10.7554/eLife.82556. PMC 9812405. PMID 36519529.
Abramson, Josh; Adler, Jonas; Dunger, Jack; Evans, Richard; Green, Tim; Pritzel, Alexander; Ronneberger, Olaf; Willmore, Lindsay; Ballard, Andrew J.; Bambrick, Joshua; Bodenstein, Sebastian W.; Evans, David A.; Hung, Chia-Chun; O'Neill, Michael; Reiman, David (June 2024). "Accurate structure prediction of biomolecular interactions with AlphaFold 3". Nature. 630 (8016): 493–500. Bibcode:2024Natur.630..493A. doi:10.1038/s41586-024-07487-w. ISSN 1476-4687. PMC 11168924. PMID 38718835.
An, Hyun Joo; Froehlich, John W; Lebrilla, Carlito B (2009-10-01). "Determination of glycosylation sites and site-specific heterogeneity in glycoproteins". Current Opinion in Chemical Biology. Analytical Techniques/Mechanisms. 13 (4): 421–426. doi:10.1016/j.cbpa.2009.07.022. ISSN 1367-5931. PMC 2749913. PMID 19700364.
Masters, Matthew R.; Mahmoud, Amr H.; Lill, Markus A. (2025-10-06). "Investigating whether deep learning models for co-folding learn the physics of protein-ligand interactions". Nature Communications. 16 (1): 8854. Bibcode:2025NatCo..16.8854M. doi:10.1038/s41467-025-63947-5. ISSN 2041-1723. PMC 12501370. PMID 41053181.
Dabrowski-Tumanski, Pawel; Stasiak, Andrzej (7 November 2023). "AlphaFold Blindness to Topological Barriers Affects Its Ability to Correctly Predict Proteins' Topology". Molecules. 28 (22): 7462. doi:10.3390/molecules28227462. PMC 10672856. PMID 38005184.
Aubel, Margaux; Eicholt, Lars; Bornberg-Bauer, Erich (2023-03-29). "Assessing structure and disorder prediction tools for de novo emerged proteins in the age of machine learning". F1000Research. 12: 347. doi:10.12688/f1000research.130443.1. PMC 10126731. PMID 37113259.
Michaud, Jennifer M.; Madani, Ali; Fraser, James S. (November 2022). "A language model beats alphafold2 on orphans". Nature Biotechnology. 40 (11): 1576–1577. doi:10.1038/s41587-022-01466-0. PMC 9669189. PMID 36192635.

nobelprize.org (Global: 265^th place; English: 450^th place)

"Press release: The Nobel Prize in Chemistry 2024". The Royal Swedish Academy of Sciences. 9 October 2024. Retrieved 29 November 2024. The Royal Swedish Academy of Sciences has decided to award the Nobel Prize in Chemistry 2024 with one half to David Baker..."for computational protein design" and the other half jointly to Demis Hassabis... John Jumper..."for protein structure prediction"
"The Nobel Prize in Chemistry 2024". NobelPrize.org. Archived from the original on 2024-10-09. Retrieved 2024-10-10.

nottingham.ac.uk (Global: 3,505^th place; English: 1,837^th place)

blogs.nottingham.ac.uk

Brigitte Nerlich, Protein folding and science communication: Between hype and humility Archived 2022-02-15 at the Wayback Machine, University of Nottingham blog, 4 December 2020

nytimes.com (Global: 7^th place; English: 7^th place)

Metz, Cade (2024-05-08). "Google Unveils A.I. for Predicting Behavior of Human Molecules". The New York Times. ISSN 0362-4331. Archived from the original on 2024-10-10. Retrieved 2024-05-09.
Cade Metz, London A.I. Lab Claims Breakthrough That Could Accelerate Drug Discovery Archived 2022-08-04 at the Wayback Machine, New York Times, 30 November 2020

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

Stephen Curry, No, DeepMind has not solved protein folding Archived 2022-07-29 at the Wayback Machine, Reciprocal Space (blog), 2 December 2020

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

See block diagram. Also John Jumper et al. (1 December 2020), AlphaFold 2 presentation Archived 2022-07-03 at the Wayback Machine, slide 10
The structure module is stated to use a "3-d equivariant transformer architecture" (John Jumper et al. (1 December 2020), AlphaFold 2 presentation Archived 2022-07-03 at the Wayback Machine, 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 Archived 2021-10-07 at the Wayback Machine, NeurIPS 2020; also website Archived 2022-07-03 at the Wayback Machine. It is not known how similar this may or may not be to what was used in AlphaFold.
See also the blog post Archived 2020-12-08 at the Wayback Machine by AlQuaraishi on this, or the more detailed post Archived 2022-07-03 at the Wayback Machine by Fabian Fuchs
John Jumper et al. (1 December 2020), AlphaFold 2 presentation Archived 2022-07-03 at the Wayback Machine, slides 12 to 20
Group performance based on combined z-scores Archived 2022-03-08 at the Wayback Machine, CASP 13, December 2018. (AlphaFold = Team 043: A7D)
See CASP 13 data tables Archived 2022-03-14 at the Wayback Machine for 043 A7D, 322 Zhang, and 089 MULTICOM
Wei Zheng et al,Deep-learning contact-map guided protein structure prediction in CASP13 Archived 2022-01-22 at the Wayback Machine, Proteins: Structure, Function, and Bioinformatics, 87(12) 1149–1164 doi:10.1002/prot.25792; and slides Archived 2022-07-26 at the Wayback Machine
Artificial intelligence solution to a 50-year-old science challenge could 'revolutionise' medical research Archived 2022-04-24 at the Wayback Machine (press release), CASP organising committee, 30 November 2020

proteinswebteam.github.io (Global: low place; English: low place)

InterPro (22 July 2021). "Alphafold Structure Predictions Available In Interpro". proteinswebteam.github.io. Archived from the original on 2021-11-05. Retrieved 2021-07-29.

sandbox.google.com (Global: low place; English: low place)

golgi.sandbox.google.com

"A non-commercial server of AlphaFold-3". Archived from the original on 2024-10-10. Retrieved 2024-05-12.

scholar.google.com (Global: 1,418^th place; English: 768^th place)

"Google Scholar". scholar.google.com. Retrieved 2025-05-01.
"Google Scholar". scholar.google.com. Retrieved 2025-05-01.

sci.am (Global: 6,786^th place; English: 9,046^th place)

issi2025.iiap.sci.am

Cui, Haochuan; Wang, Yuzhuo; Li, Kai (2025). Beyond Citations: Tracing and Validating the Rapid Adoption of AlphaFold in Biomedical Research Through Full-Text Analysis (PDF). 20th International Conference on Scientometrics and Informetrics (ISSI 2025). Yerevan, Armenia: ISSI Society. pp. 2002–2009. doi:10.51408/issi2025_147.

science.org (Global: 1,007^th place; English: 665^th place)

Robert F. Service, 'The game has changed.' AI triumphs at solving protein structures Archived 2023-06-24 at the Wayback Machine, Science, 30 November 2020

semanticscholar.org (Global: 15^th place; English: 8^th place)

api.semanticscholar.org

Stoddart, Charlotte (1 March 2022). "Structural biology: How proteins got their close-up". Knowable Magazine. doi:10.1146/knowable-022822-1. S2CID 247206999. Archived from the original on 7 April 2022. Retrieved 25 March 2022.
Callaway, Ewen (2020-11-30). "'It will change everything': DeepMind's AI makes gigantic leap in solving protein structures". Nature. 588 (7837): 203–204. Bibcode:2020Natur.588..203C. doi:10.1038/d41586-020-03348-4. PMID 33257889. S2CID 227243204.
Callaway, Ewen (13 April 2022). "What's next for AlphaFold and the AI protein-folding revolution". Nature. 604 (7905): 234–238. Bibcode:2022Natur.604..234C. doi:10.1038/d41586-022-00997-5. PMID 35418629. S2CID 248156195.
Singh, Arunima (2020). "Deep learning 3D structures". Nature Methods. 17 (3): 249. doi:10.1038/s41592-020-0779-y. ISSN 1548-7105. PMID 32132733. S2CID 212403708.
Callaway, Ewen (2022-12-13). "After AlphaFold: protein-folding contest seeks next big breakthrough". Nature. 613 (7942): 13–14. doi:10.1038/d41586-022-04438-1. PMID 36513827. S2CID 254660427.
Callaway, Ewen (2022-07-28). "'The entire protein universe': AI predicts shape of nearly every known protein". Nature. 608 (7921): 15–16. Bibcode:2022Natur.608...15C. doi:10.1038/d41586-022-02083-2. PMID 35902752. S2CID 251159714.
Bagdonas, Haroldas; Fogarty, Carl A.; Fadda, Elisa; Agirre, Jon (2021-10-29). "The case for post-predictional modifications in the AlphaFold Protein Structure Database" (PDF). Nature Structural & Molecular Biology. 28 (11): 869–870. doi:10.1038/s41594-021-00680-9. ISSN 1545-9985. PMID 34716446. S2CID 240228913. Archived (PDF) from the original on 2024-01-13. Retrieved 2024-01-13.

skeptic.org.uk (Global: low place; English: low place)

Briggs, David (2020-12-04). "If Google's Alphafold2 really has solved the protein folding problem, they need to show their working". The Skeptic. Archived from the original on 2024-05-12. Retrieved 2024-05-12.

ssrn.com (Global: 907^th place; English: 529^th place)

papers.ssrn.com

Yu, Zhengyi (February 2026). The Impacts of AI at Scale: Evidence from Research Scientists (Working paper). CESifo Working Papers. Munich: CESifo. ISSN 2364-1428. SSRN 6190983.

technologyreview.com (Global: 2,515^th place; English: 1,716^th place)

"DeepMind's protein-folding AI has solved a 50-year-old grand challenge of biology". MIT Technology Review. Archived from the original on 2021-08-28. Retrieved 2020-11-30.
"DeepMind's protein-folding AI has solved a 50-year-old grand challenge of biology". MIT Technology Review. Archived from the original on 28 August 2021. Retrieved 30 November 2020.

telegraph.co.uk (Global: 40^th place; English: 27^th place)

Lizzie Roberts, 'Once in a generation advance' as Google AI researchers crack 50-year-old biological challenge Archived 2022-08-04 at the Wayback Machine. Daily Telegraph, 30 November 2020

theguardian.com (Global: 16^th place; English: 11^th place)

Sample, Ian (2 December 2018). "Google's DeepMind predicts 3D shapes of proteins". The Guardian. Archived from the original on 18 July 2019. Retrieved 30 November 2020.
Ian Sample,DeepMind AI cracks 50-year-old problem of protein folding Archived 2020-11-30 at the Wayback Machine, The Guardian, 30 November 2020
Sample, Ian (2023-09-21). "Team behind AI program AlphaFold win Lasker science prize". The Guardian. ISSN 0261-3077. Archived from the original on 2024-10-10. Retrieved 2024-05-09.

thetimes.com (Global: 221^st place; English: 110^th place)

Tom Whipple, Deepmind finds biology's 'holy grail' with answer to protein problem, The Times (online), 30 November 2020.
Tom Whipple wrote six articles on the subject for The Times when the news broke. (thread Archived 2021-11-08 at the Wayback Machine).

twitter.com (Global: 43^rd place; English: 43^rd place)

Mohammed AlQuraishi, CASP14 scores just came out and they're astounding Archived 2022-08-04 at the Wayback Machine, Twitter, 30 November 2020.
Tom Whipple, Deepmind finds biology's 'holy grail' with answer to protein problem, The Times (online), 30 November 2020.
Tom Whipple wrote six articles on the subject for The Times when the news broke. (thread Archived 2021-11-08 at the Wayback Machine).
Demis Hassabis, "Brief update on some exciting progress on #AlphaFold!" Archived 2022-07-22 at the Wayback Machine (tweet), via twitter, 18 June 2021

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

"AlphaFold". Deepmind. Archived from the original on 19 January 2021. Retrieved 30 November 2020.
"DeepMind's protein-folding AI has solved a 50-year-old grand challenge of biology". MIT Technology Review. Archived from the original on 2021-08-28. Retrieved 2020-11-30.
Shead, Sam (2020-11-30). "DeepMind solves 50-year-old 'grand challenge' with protein folding A.I." CNBC. Archived from the original on 2021-01-28. Retrieved 2020-11-30.
Stoddart, Charlotte (1 March 2022). "Structural biology: How proteins got their close-up". Knowable Magazine. doi:10.1146/knowable-022822-1. S2CID 247206999. Archived from the original on 7 April 2022. Retrieved 25 March 2022.
Robert F. Service, 'The game has changed.' AI triumphs at solving protein structures Archived 2023-06-24 at the Wayback Machine, Science, 30 November 2020
Mohammed AlQuraishi, CASP14 scores just came out and they're astounding Archived 2022-08-04 at the Wayback Machine, Twitter, 30 November 2020.
Stephen Curry, No, DeepMind has not solved protein folding Archived 2022-07-29 at the Wayback Machine, Reciprocal Space (blog), 2 December 2020
Ball, Phillip (9 December 2020). "Behind the screens of AlphaFold". Chemistry World. Archived from the original on 15 August 2021. Retrieved 10 December 2020.
"GitHub - deepmind/alphafold: Open source code for AlphaFold". GitHub. Archived from the original on 2021-07-23. Retrieved 2021-07-24.
"AlphaFold Protein Structure Database". alphafold.ebi.ac.uk. Archived from the original on 2021-07-24. Retrieved 2021-07-24.
"AlphaFold 3 predicts the structure and interactions of all of life's molecules". Google. 2024-05-08. Archived from the original on 2024-05-09. Retrieved 2024-05-09.
Hunt, Christian Edwards, Katie (9 October 2024). "Scientists who used AI to 'crack the code' of almost all proteins win Nobel Prize in chemistry". CNN. Archived from the original on 10 October 2024. Retrieved 9 October 2024.{{cite news}}: CS1 maint: multiple names: authors list (link)
Knapp, Alex. "2023 Breakthrough Prizes Announced: Deepmind's Protein Folders Awarded $3 Million". Forbes. Archived from the original on 2024-05-09. Retrieved 2024-05-09.
"AlphaFold: Using AI for scientific discovery". Deepmind. 15 January 2020. Archived from the original on 2022-03-07. Retrieved 2020-11-30.
Mohammed AlQuraishi (May 2019), AlphaFold at CASP13 Archived 2021-11-22 at the Wayback Machine, Bioinformatics, 35(22), 4862–4865 doi:10.1093/bioinformatics/btz422. See also Mohammed AlQuraishi (December 9, 2018), AlphaFold @ CASP13: "What just happened?" Archived 2022-07-29 at the Wayback Machine (blog post).
Mohammed AlQuraishi (15 January 2020), A watershed moment for protein structure prediction Archived 2022-06-23 at the Wayback Machine, Nature 577, 627–628 doi:10.1038/d41586-019-03951-0
AlphaFold: Machine learning for protein structure prediction Archived 2022-05-12 at the Wayback Machine, Foldit, 31 January 2020
"DeepMind is answering one of biology's biggest challenges". The Economist. 2020-11-30. ISSN 0013-0613. Archived from the original on 2020-12-03. Retrieved 2020-11-30.
Jeremy Kahn, Lessons from DeepMind's breakthrough in protein-folding A.I. Archived 2022-04-08 at the Wayback Machine, Fortune, 1 December 2020
See block diagram. Also John Jumper et al. (1 December 2020), AlphaFold 2 presentation Archived 2022-07-03 at the Wayback Machine, slide 10
The structure module is stated to use a "3-d equivariant transformer architecture" (John Jumper et al. (1 December 2020), AlphaFold 2 presentation Archived 2022-07-03 at the Wayback Machine, 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 Archived 2021-10-07 at the Wayback Machine, NeurIPS 2020; also website Archived 2022-07-03 at the Wayback Machine. It is not known how similar this may or may not be to what was used in AlphaFold.
See also the blog post Archived 2020-12-08 at the Wayback Machine by AlQuaraishi on this, or the more detailed post Archived 2022-07-03 at the Wayback Machine by Fabian Fuchs
John Jumper et al. (1 December 2020), AlphaFold 2 presentation Archived 2022-07-03 at the Wayback Machine, slides 12 to 20
Metz, Cade (2024-05-08). "Google Unveils A.I. for Predicting Behavior of Human Molecules". The New York Times. ISSN 0362-4331. Archived from the original on 2024-10-10. Retrieved 2024-05-09.
"A non-commercial server of AlphaFold-3". Archived from the original on 2024-10-10. Retrieved 2024-05-12.
Group performance based on combined z-scores Archived 2022-03-08 at the Wayback Machine, CASP 13, December 2018. (AlphaFold = Team 043: A7D)
Sample, Ian (2 December 2018). "Google's DeepMind predicts 3D shapes of proteins". The Guardian. Archived from the original on 18 July 2019. Retrieved 30 November 2020.
"AlphaFold: Using AI for scientific discovery". Deepmind. January 2020. Archived from the original on 10 October 2024. Retrieved 30 November 2020.
See CASP 13 data tables Archived 2022-03-14 at the Wayback Machine for 043 A7D, 322 Zhang, and 089 MULTICOM
Wei Zheng et al,Deep-learning contact-map guided protein structure prediction in CASP13 Archived 2022-01-22 at the Wayback Machine, Proteins: Structure, Function, and Bioinformatics, 87(12) 1149–1164 doi:10.1002/prot.25792; and slides Archived 2022-07-26 at the Wayback Machine
"DeepMind Breakthrough Helps to Solve How Diseases Invade Cells". Bloomberg.com. 2020-11-30. Archived from the original on 2022-04-05. Retrieved 2020-11-30.
"deepmind/deepmind-research". GitHub. Archived from the original on 2022-02-01. Retrieved 2020-11-30.
"AlphaFold: a solution to a 50-year-old grand challenge in biology". Deepmind. 30 November 2020. Archived from the original on 30 November 2020. Retrieved 30 November 2020.
"DeepMind's protein-folding AI has solved a 50-year-old grand challenge of biology". MIT Technology Review. Archived from the original on 28 August 2021. Retrieved 30 November 2020.
Artificial intelligence solution to a 50-year-old science challenge could 'revolutionise' medical research Archived 2022-04-24 at the Wayback Machine (press release), CASP organising committee, 30 November 2020
Brigitte Nerlich, Protein folding and science communication: Between hype and humility Archived 2022-02-15 at the Wayback Machine, University of Nottingham blog, 4 December 2020
Michael Le Page, DeepMind's AI biologist can decipher secrets of the machinery of life Archived 2022-08-02 at the Wayback Machine, New Scientist, 30 November 2020
The predictions of DeepMind's latest AI could revolutionise medicine Archived 2021-11-07 at the Wayback Machine, New Scientist, 2 December 2020
Tom Whipple, Deepmind finds biology's 'holy grail' with answer to protein problem, The Times (online), 30 November 2020.
Tom Whipple wrote six articles on the subject for The Times when the news broke. (thread Archived 2021-11-08 at the Wayback Machine).
Cade Metz, London A.I. Lab Claims Breakthrough That Could Accelerate Drug Discovery Archived 2022-08-04 at the Wayback Machine, New York Times, 30 November 2020
Ian Sample,DeepMind AI cracks 50-year-old problem of protein folding Archived 2020-11-30 at the Wayback Machine, The Guardian, 30 November 2020
Lizzie Roberts, 'Once in a generation advance' as Google AI researchers crack 50-year-old biological challenge Archived 2022-08-04 at the Wayback Machine. Daily Telegraph, 30 November 2020
Tim Hubbard, The secret of life, part 2: the solution of the protein folding problem. Archived 2022-05-14 at the Wayback Machine, medium.com, 30 November 2020
e.g. Greg Bowman, Protein folding and related problems remain unsolved despite AlphaFold's advance Archived 2022-07-13 at the Wayback Machine, Folding@home blog, 8 December 2020
Sample, Ian (2023-09-21). "Team behind AI program AlphaFold win Lasker science prize". The Guardian. ISSN 0261-3077. Archived from the original on 2024-10-10. Retrieved 2024-05-09.
"The Nobel Prize in Chemistry 2024". NobelPrize.org. Archived from the original on 2024-10-09. Retrieved 2024-10-10.
Domínguez, Nuño (2020-12-02). "La inteligencia artificial arrasa en uno de los problemas más importantes de la biología". El País (in Spanish). Archived from the original on 2022-07-26. Retrieved 2024-05-12.
Briggs, David (2020-12-04). "If Google's Alphafold2 really has solved the protein folding problem, they need to show their working". The Skeptic. Archived from the original on 2024-05-12. Retrieved 2024-05-12.
Demis Hassabis, "Brief update on some exciting progress on #AlphaFold!" Archived 2022-07-22 at the Wayback Machine (tweet), via twitter, 18 June 2021
"AlphaFold Protein Structure Database". alphafold.ebi.ac.uk. Archived from the original on 2022-07-29. Retrieved 2021-07-29.
"AlphaFold Protein Structure Database". alphafold.ebi.ac.uk. Archived from the original on 29 July 2022. Retrieved 27 July 2021.
"AlphaFold Protein Structure Database". www.alphafold.ebi.ac.uk. Archived from the original on 2022-08-02. Retrieved 2021-07-24.
InterPro (22 July 2021). "Alphafold Structure Predictions Available In Interpro". proteinswebteam.github.io. Archived from the original on 2021-11-05. Retrieved 2021-07-29.
"Putting the power of AlphaFold into the world's hands". Deepmind. 22 July 2022. Archived from the original on 24 July 2021. Retrieved 24 July 2021.
"What use cases does AlphaFold not support?". AlphaFold Protein Structure Database. Archived from the original on 2022-07-29. Retrieved 2022-05-15.
AlphaFold heralds a data-driven revolution in biology and medicine Archived 2024-10-10 at the Wayback Machine, by Janet M. Thornton, Roman A. Laskowski & Neera Borkakoti, Nature Medicine, volume 12, pages 1666–1669, 12 October 2021
McBride, John M.; Polev, Konstantin; Abdirasulov, Amirbek; Reinharz, Vladimir; Grzybowski, Bartosz A.; Tlusty, Tsvi (2023-11-20). "AlphaFold2 Can Predict Single-Mutation Effects". Physical Review Letters. 131 (21) 218401. arXiv:2204.06860. Bibcode:2023PhRvL.131u8401M. doi:10.1103/PhysRevLett.131.218401. ISSN 0031-9007. PMID 38072605. Archived from the original on 2024-06-09. Retrieved 2023-11-26.
Bagdonas, Haroldas; Fogarty, Carl A.; Fadda, Elisa; Agirre, Jon (2021-10-29). "The case for post-predictional modifications in the AlphaFold Protein Structure Database" (PDF). Nature Structural & Molecular Biology. 28 (11): 869–870. doi:10.1038/s41594-021-00680-9. ISSN 1545-9985. PMID 34716446. S2CID 240228913. Archived (PDF) from the original on 2024-01-13. Retrieved 2024-01-13.
"DeepMind's latest AI breakthrough could turbocharge drug discovery". Fast Company. ISSN 1085-9241. Archived from the original on 2023-01-24. Retrieved 2023-01-24.
"AI Can Help Scientists Find a Covid-19 Vaccine". Wired. ISSN 1059-1028. Archived from the original on 2022-04-23. Retrieved 2020-12-01.
"Computational predictions of protein structures associated with COVID-19". Deepmind. 4 August 2020. Archived from the original on 2022-03-25. Retrieved 2020-12-01.
"How DeepMind's new protein-folding A.I. is already helping to combat the coronavirus pandemic". Fortune. Archived from the original on 2020-11-30. Retrieved 2020-12-01.

wired.com (Global: 254^th place; English: 178^th place)

"AI Can Help Scientists Find a Covid-19 Vaccine". Wired. ISSN 1059-1028. Archived from the original on 2022-04-23. Retrieved 2020-12-01.

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

search.worldcat.org

Abramson, Josh; Adler, Jonas; Dunger, Jack; Evans, Richard; Green, Tim; Pritzel, Alexander; Ronneberger, Olaf; Willmore, Lindsay; Ballard, Andrew J.; Bambrick, Joshua; Bodenstein, Sebastian W.; Evans, David A.; Hung, Chia-Chun; O'Neill, Michael; Reiman, David (2024-05-08). "Accurate structure prediction of biomolecular interactions with AlphaFold 3". Nature. 630 (8016): 493–500. Bibcode:2024Natur.630..493A. doi:10.1038/s41586-024-07487-w. ISSN 1476-4687. PMC 11168924. PMID 38718835.
Jumper, John; et al. (August 2021). "Highly accurate protein structure prediction with AlphaFold". Nature. 596 (7873): 583–589. Bibcode:2021Natur.596..583J. doi:10.1038/s41586-021-03819-2. ISSN 1476-4687. PMC 8371605. PMID 34265844.
"DeepMind is answering one of biology's biggest challenges". The Economist. 2020-11-30. ISSN 0013-0613. Archived from the original on 2020-12-03. Retrieved 2020-11-30.
Metz, Cade (2024-05-08). "Google Unveils A.I. for Predicting Behavior of Human Molecules". The New York Times. ISSN 0362-4331. Archived from the original on 2024-10-10. Retrieved 2024-05-09.
Singh, Arunima (2020). "Deep learning 3D structures". Nature Methods. 17 (3): 249. doi:10.1038/s41592-020-0779-y. ISSN 1548-7105. PMID 32132733. S2CID 212403708.
Hou, Jie; Wu, Tianqi; Cao, Renzhi; Cheng, Jianlin (2019-04-25). "Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13". Proteins: Structure, Function, and Bioinformatics. 87 (12). Wiley: 1165–1178. bioRxiv 10.1101/552422. doi:10.1002/prot.25697. ISSN 0887-3585. PMC 6800999. PMID 30985027.
Sample, Ian (2023-09-21). "Team behind AI program AlphaFold win Lasker science prize". The Guardian. ISSN 0261-3077. Archived from the original on 2024-10-10. Retrieved 2024-05-09.
Yu, Zhengyi (February 2026). The Impacts of AI at Scale: Evidence from Research Scientists (Working paper). CESifo Working Papers. Munich: CESifo. ISSN 2364-1428. SSRN 6190983.
Guo, Song-Bin; Meng, Yuan; Lin, Liteng; Zhou, Zhen-Zhong; Li, Hai-Long; Tian, Xiao-Peng; Huang, Wei-Juan (2024-10-05). "Artificial intelligence alphafold model for molecular biology and drug discovery: a machine-learning-driven informatics investigation". Molecular Cancer. 23 (1): 223. doi:10.1186/s12943-024-02140-6. ISSN 1476-4598. PMC 11452995. PMID 39369244.
Steinegger, Martin; Söding, Johannes (2017). "MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets". Nature Biotechnology. 35 (11): 1026–1028. doi:10.1038/nbt.3988. hdl:11858/00-001M-0000-002E-1967-3. ISSN 1087-0156. PMID 29035372.
McBride, John M.; Polev, Konstantin; Abdirasulov, Amirbek; Reinharz, Vladimir; Grzybowski, Bartosz A.; Tlusty, Tsvi (2023-11-20). "AlphaFold2 Can Predict Single-Mutation Effects". Physical Review Letters. 131 (21) 218401. arXiv:2204.06860. Bibcode:2023PhRvL.131u8401M. doi:10.1103/PhysRevLett.131.218401. ISSN 0031-9007. PMID 38072605. Archived from the original on 2024-06-09. Retrieved 2023-11-26.
McBride, John M.; Tlusty, Tsvi (2024-08-26). "AI-Predicted Protein Deformation Encodes Energy Landscape Perturbation". Physical Review Letters. 133 (9) 098401. arXiv:2311.18222. Bibcode:2024PhRvL.133i8401M. doi:10.1103/PhysRevLett.133.098401. ISSN 0031-9007. PMID 39270162.
Abramson, Josh; Adler, Jonas; Dunger, Jack; Evans, Richard; Green, Tim; Pritzel, Alexander; Ronneberger, Olaf; Willmore, Lindsay; Ballard, Andrew J.; Bambrick, Joshua; Bodenstein, Sebastian W.; Evans, David A.; Hung, Chia-Chun; O'Neill, Michael; Reiman, David (June 2024). "Accurate structure prediction of biomolecular interactions with AlphaFold 3". Nature. 630 (8016): 493–500. Bibcode:2024Natur.630..493A. doi:10.1038/s41586-024-07487-w. ISSN 1476-4687. PMC 11168924. PMID 38718835.
Bagdonas, Haroldas; Fogarty, Carl A.; Fadda, Elisa; Agirre, Jon (2021-10-29). "The case for post-predictional modifications in the AlphaFold Protein Structure Database" (PDF). Nature Structural & Molecular Biology. 28 (11): 869–870. doi:10.1038/s41594-021-00680-9. ISSN 1545-9985. PMID 34716446. S2CID 240228913. Archived (PDF) from the original on 2024-01-13. Retrieved 2024-01-13.
An, Hyun Joo; Froehlich, John W; Lebrilla, Carlito B (2009-10-01). "Determination of glycosylation sites and site-specific heterogeneity in glycoproteins". Current Opinion in Chemical Biology. Analytical Techniques/Mechanisms. 13 (4): 421–426. doi:10.1016/j.cbpa.2009.07.022. ISSN 1367-5931. PMC 2749913. PMID 19700364.
Masters, Matthew R.; Mahmoud, Amr H.; Lill, Markus A. (2025-10-06). "Investigating whether deep learning models for co-folding learn the physics of protein-ligand interactions". Nature Communications. 16 (1): 8854. Bibcode:2025NatCo..16.8854M. doi:10.1038/s41467-025-63947-5. ISSN 2041-1723. PMC 12501370. PMID 41053181.
"DeepMind's latest AI breakthrough could turbocharge drug discovery". Fast Company. ISSN 1085-9241. Archived from the original on 2023-01-24. Retrieved 2023-01-24.
"AI Can Help Scientists Find a Covid-19 Vaccine". Wired. ISSN 1059-1028. Archived from the original on 2022-04-23. Retrieved 2020-12-01.

AlphaFold (English Wikipedia)

adsabs.harvard.edu (Global: 14th place; English: 9th place)

ui.adsabs.harvard.edu

aps.org (Global: 1,264th place; English: 951st place)

link.aps.org

arxiv.org (Global: 49th place; English: 31st place)

blog.google (Global: 4,904th place; English: 4,208th place)

bloomberg.com (Global: 118th place; English: 85th place)

carolynstein.github.io (Global: low place; English: low place)

chemh.com (Global: low place; English: low place)

chemistryworld.com (Global: low place; English: 8,914th place)

cnbc.com (Global: 303rd place; English: 191st place)

cnn.com (Global: 36th place; English: 26th place)

edition.cnn.com

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

deepmind.google (Global: low place; English: low place)

doi.org (Global: 2nd place; English: 2nd place)

ebi.ac.uk (Global: 4,422nd place; English: 7,719th place)

alphafold.ebi.ac.uk

economist.com (Global: 331st place; English: 272nd place)

elpais.com (Global: 89th place; English: 268th place)

engadget.com (Global: 613th place; English: 442nd place)

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

fastcompany.com (Global: 1,399th place; English: 772nd place)

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

foldingathome.org (Global: low place; English: low place)

forbes.com (Global: 71st place; English: 54th place)

fortune.com (Global: 647th place; English: 421st place)

ft.com (Global: 193rd place; English: 127th place)

github.com (Global: 380th place; English: 337th place)

hdl.handle.net (Global: 66th place; English: 34th place)

hms.harvard.edu (Global: low place; English: 7,550th place)

ccsp.hms.harvard.edu

innovationgrowthlab.org (Global: low place; English: low place)

knowablemagazine.org (Global: low place; English: 9,041st place)

maynoothuniversity.ie (Global: low place; English: low place)

mural.maynoothuniversity.ie

medium.com (Global: 651st place; English: 479th place)

timjph.medium.com

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

nature.com (Global: 207th place; English: 347th place)

nber.org (Global: 3,811th place; English: 2,424th place)

newscientist.com (Global: 1,041st place; English: 901st place)

nih.gov (Global: 5th place; English: 5th place)

pubmed.ncbi.nlm.nih.gov

ncbi.nlm.nih.gov

nobelprize.org (Global: 265th place; English: 450th place)

nottingham.ac.uk (Global: 3,505th place; English: 1,837th place)

blogs.nottingham.ac.uk

nytimes.com (Global: 7th place; English: 7th place)

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

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

proteinswebteam.github.io (Global: low place; English: low place)

sandbox.google.com (Global: low place; English: low place)

golgi.sandbox.google.com

scholar.google.com (Global: 1,418th place; English: 768th place)

sci.am (Global: 6,786th place; English: 9,046th place)

issi2025.iiap.sci.am

science.org (Global: 1,007th place; English: 665th place)

semanticscholar.org (Global: 15th place; English: 8th place)

api.semanticscholar.org

skeptic.org.uk (Global: low place; English: low place)

ssrn.com (Global: 907th place; English: 529th place)

papers.ssrn.com

technologyreview.com (Global: 2,515th place; English: 1,716th place)

telegraph.co.uk (Global: 40th place; English: 27th place)

theguardian.com (Global: 16th place; English: 11th place)

thetimes.com (Global: 221st place; English: 110th place)

twitter.com (Global: 43rd place; English: 43rd place)

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

wired.com (Global: 254th place; English: 178th place)

worldcat.org (Global: 4th place; English: 4th place)

search.worldcat.org

adsabs.harvard.edu (Global: 14^th place; English: 9^th place)

aps.org (Global: 1,264^th place; English: 951^st place)

arxiv.org (Global: 49^th place; English: 31^st place)

blog.google (Global: 4,904^th place; English: 4,208^th place)

bloomberg.com (Global: 118^th place; English: 85^th place)

chemistryworld.com (Global: low place; English: 8,914^th place)

cnbc.com (Global: 303^rd place; English: 191^st place)

cnn.com (Global: 36^th place; English: 26^th place)

doi.org (Global: 2^nd place; English: 2^nd place)

ebi.ac.uk (Global: 4,422^nd place; English: 7,719^th place)

economist.com (Global: 331^st place; English: 272^nd place)

elpais.com (Global: 89^th place; English: 268^th place)

engadget.com (Global: 613^th place; English: 442^nd place)

fastcompany.com (Global: 1,399^th place; English: 772^nd place)

forbes.com (Global: 71^st place; English: 54^th place)

fortune.com (Global: 647^th place; English: 421^st place)

ft.com (Global: 193^rd place; English: 127^th place)

github.com (Global: 380^th place; English: 337^th place)

hdl.handle.net (Global: 66^th place; English: 34^th place)

hms.harvard.edu (Global: low place; English: 7,550^th place)

knowablemagazine.org (Global: low place; English: 9,041^st place)

medium.com (Global: 651^st place; English: 479^th place)

nature.com (Global: 207^th place; English: 347^th place)

nber.org (Global: 3,811^th place; English: 2,424^th place)

newscientist.com (Global: 1,041^st place; English: 901^st place)

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

nobelprize.org (Global: 265^th place; English: 450^th place)

nottingham.ac.uk (Global: 3,505^th place; English: 1,837^th place)

nytimes.com (Global: 7^th place; English: 7^th place)

scholar.google.com (Global: 1,418^th place; English: 768^th place)

sci.am (Global: 6,786^th place; English: 9,046^th place)

science.org (Global: 1,007^th place; English: 665^th place)

semanticscholar.org (Global: 15^th place; English: 8^th place)

ssrn.com (Global: 907^th place; English: 529^th place)

technologyreview.com (Global: 2,515^th place; English: 1,716^th place)

telegraph.co.uk (Global: 40^th place; English: 27^th place)

theguardian.com (Global: 16^th place; English: 11^th place)

thetimes.com (Global: 221^st place; English: 110^th place)

twitter.com (Global: 43^rd place; English: 43^rd place)

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

wired.com (Global: 254^th place; English: 178^th place)

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