AlphaFold (English Wikipedia)

The structure module is stated to use a "3-d equivariant transformer architecture" (John Jumper et al. (1 December 2020), AlphaFold 2 presentation, slide 12).
One design for a transformer network with SE(3)-equivariance was proposed in Fabian Fuchs et al SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks, NeurIPS 2020; also website. It is not known how similar this may or may not be to what was used in AlphaFold.
See also the blog post by AlQuaraishi on this, or the more detailed post by Fabian Fuchs
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.

blog.google

"AlphaFold 3 predicts the structure and interactions of all of life's molecules". Google. 2024-05-08. Retrieved 2024-05-09.

bloomberg.com

"DeepMind Breakthrough Helps to Solve How Diseases Invade Cells". Bloomberg.com. 2020-11-30. Retrieved 2020-11-30.

chemistryworld.com

Balls, Phillip (9 December 2020). "Behind the screens of AlphaFold". Chemistry World.

cnbc.com

Shead, Sam (2020-11-30). "DeepMind solves 50-year-old 'grand challenge' with protein folding A.I." CNBC. Retrieved 2020-11-30.

deepmind.com

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

doi.org

Stoddart, Charlotte (1 March 2022). "Structural biology: How proteins got their close-up". Knowable Magazine. doi:10.1146/knowable-022822-1. S2CID 247206999. 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.
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.
Mohammed AlQuraishi (May 2019), AlphaFold at CASP13, Bioinformatics, 35(22), 4862–4865 doi:10.1093/bioinformatics/btz422. See also Mohammed AlQuraishi (December 9, 2018), AlphaFold @ CASP13: "What just happened?" (blog post).
Mohammed AlQuraishi (15 January 2020), A watershed moment for protein structure prediction, Nature 577, 627–628 doi:10.1038/d41586-019-03951-0
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.
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: 1–3. doi:10.1038/s41586-024-07487-w. ISSN 1476-4687. PMC 11168924.
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, Proteins: Structure, Function, and Bioinformatics, 87(12) 1149–1164 doi:10.1002/prot.25792; and slides
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.
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.
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.
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.
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.
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. doi:10.1038/s41592-022-01685-y. PMC 9911346. PMID 36424442.
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.

ebi.ac.uk

alphafold.ebi.ac.uk

"AlphaFold Protein Structure Database". alphafold.ebi.ac.uk. Retrieved 2021-07-24.
"AlphaFold Protein Structure Database". alphafold.ebi.ac.uk. Retrieved 2021-07-29.
"AlphaFold Protein Structure Database". alphafold.ebi.ac.uk. Retrieved 27 July 2021.
"AlphaFold Protein Structure Database". www.alphafold.ebi.ac.uk.
"What use cases does AlphaFold not support?". AlphaFold Protein Structure Database.

economist.com

"DeepMind is answering one of biology's biggest challenges". The Economist. 2020-11-30. ISSN 0013-0613. Retrieved 2020-11-30.

elpais.com

Domínguez, Nuño (2020-12-02). "La inteligencia artificial arrasa en uno de los problemas más importantes de la biología". El País (in Spanish). Retrieved 2024-05-12.

fabianfuchsml.github.io

The structure module is stated to use a "3-d equivariant transformer architecture" (John Jumper et al. (1 December 2020), AlphaFold 2 presentation, slide 12).
One design for a transformer network with SE(3)-equivariance was proposed in Fabian Fuchs et al SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks, NeurIPS 2020; also website. It is not known how similar this may or may not be to what was used in AlphaFold.
See also the blog post by AlQuaraishi on this, or the more detailed post by Fabian Fuchs

fastcompany.com

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

fold.it

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

foldingathome.org

e.g. Greg Bowman, Protein folding and related problems remain unsolved despite AlphaFold's advance, Folding@home blog, 8 December 2020

forbes.com

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

fortune.com

Jeremy Kahn, Lessons from DeepMind's breakthrough in protein-folding A.I., Fortune, 1 December 2020
"How DeepMind's new protein-folding A.I. is already helping to combat the coronavirus pandemic". Fortune. Retrieved 2020-12-01.

frontlinegenomics.com

AlphaFold 3: Stepping into the future of structure prediction

github.com

"GitHub - deepmind/alphafold: Open source code for AlphaFold". GitHub. Retrieved 2021-07-24.
"deepmind/deepmind-research". GitHub. Retrieved 2020-11-30.

harvard.edu

ui.adsabs.harvard.edu

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.
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.
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.
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.
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.

ccsp.hms.harvard.edu

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

knowablemagazine.org

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

maynoothuniversity.ie

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.

medium.com

timjph.medium.com

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

moalquraishi.wordpress.com

Mohammed AlQuraishi (May 2019), AlphaFold at CASP13, Bioinformatics, 35(22), 4862–4865 doi:10.1093/bioinformatics/btz422. See also Mohammed AlQuraishi (December 9, 2018), AlphaFold @ CASP13: "What just happened?" (blog post).
Mohammed AlQuraishi (15 January 2020), A watershed moment for protein structure prediction, Nature 577, 627–628 doi:10.1038/d41586-019-03951-0
The structure module is stated to use a "3-d equivariant transformer architecture" (John Jumper et al. (1 December 2020), AlphaFold 2 presentation, slide 12).
One design for a transformer network with SE(3)-equivariance was proposed in Fabian Fuchs et al SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks, NeurIPS 2020; also website. It is not known how similar this may or may not be to what was used in AlphaFold.
See also the blog post by AlQuaraishi on this, or the more detailed post by Fabian Fuchs

nature.com

Mohammed AlQuraishi (May 2019), AlphaFold at CASP13, Bioinformatics, 35(22), 4862–4865 doi:10.1093/bioinformatics/btz422. See also Mohammed AlQuraishi (December 9, 2018), AlphaFold @ CASP13: "What just happened?" (blog post).
Mohammed AlQuraishi (15 January 2020), A watershed moment for protein structure prediction, Nature 577, 627–628 doi:10.1038/d41586-019-03951-0
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: 1–3. doi:10.1038/s41586-024-07487-w. ISSN 1476-4687. PMC 11168924.
Accurate structure prediction of biomolecular interactions with AlphaFold 3, pdf of preprint of the article in Nature.
AlphaFold3 — why did Nature publish it without its code?
AlphaFold heralds a data-driven revolution in biology and medicine, by Janet M. Thornton, Roman A. Laskowski & Neera Borkakoti, Nature Medicine, volume 12, pages 1666–1669, 12 October 2021

newscientist.com

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

nih.gov

pubmed.ncbi.nlm.nih.gov

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.
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.
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, Proteins: Structure, Function, and Bioinformatics, 87(12) 1149–1164 doi:10.1002/prot.25792; and slides
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.
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.
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.
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.
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.
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. doi:10.1038/s41592-022-01685-y. PMC 9911346. PMID 36424442.
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.

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.
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.
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: 1–3. doi:10.1038/s41586-024-07487-w. ISSN 1476-4687. PMC 11168924.
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.
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.
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. doi:10.1038/s41592-022-01685-y. PMC 9911346. PMID 36424442.
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.

nottingham.ac.uk

blogs.nottingham.ac.uk

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

nytimes.com

Metz, Cade (2024-05-08). "Google Unveils A.I. for Predicting Behavior of Human Molecules". The New York Times. ISSN 0362-4331. Retrieved 2024-05-09.
Cade Metz, London A.I. Lab Claims Breakthrough That Could Accelerate Drug Discovery, New York Times, 30 November 2020

occamstypewriter.org

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

predictioncenter.org

See block diagram. Also John Jumper et al. (1 December 2020), AlphaFold 2 presentation, 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, slide 12).
One design for a transformer network with SE(3)-equivariance was proposed in Fabian Fuchs et al SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks, NeurIPS 2020; also website. It is not known how similar this may or may not be to what was used in AlphaFold.
See also the blog post by AlQuaraishi on this, or the more detailed post by Fabian Fuchs
John Jumper et al. (1 December 2020), AlphaFold 2 presentation, slides 12 to 20
Group performance based on combined z-scores, CASP 13, December 2018. (AlphaFold = Team 043: A7D)
See CASP 13 data tables for 043 A7D, 322 Zhang, and 089 MULTICOM
Wei Zheng et al,Deep-learning contact-map guided protein structure prediction in CASP13, Proteins: Structure, Function, and Bioinformatics, 87(12) 1149–1164 doi:10.1002/prot.25792; and slides
Artificial intelligence solution to a 50-year-old science challenge could 'revolutionise' medical research (press release), CASP organising committee, 30 November 2020

proteinswebteam.github.io

InterPro (22 July 2021). "Alphafold Structure Predictions Available In Interpro". proteinswebteam.github.io. Retrieved 2021-07-29.

sandbox.google.com

golgi.sandbox.google.com

A non-commercial server of AlphaFold-3

science.org

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

semanticscholar.org

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. Retrieved 25 March 2022.
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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.

skeptic.org.uk

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

technologyreview.com

"DeepMind's protein-folding AI has solved a 50-year-old grand challenge of biology". MIT Technology Review. Retrieved 2020-11-30.
"DeepMind's protein-folding AI has solved a 50-year-old grand challenge of biology". MIT Technology Review. Retrieved 30 November 2020.

telegraph.co.uk

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

theguardian.com

Sample, Ian (2 December 2018). "Google's DeepMind predicts 3D shapes of proteins". The Guardian. Retrieved 30 November 2020.
Ian Sample,DeepMind AI cracks 50-year-old problem of protein folding, The Guardian, 30 November 2020
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thetimes.co.uk

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

twitter.com

Mohammed AlQuraishi, CASP14 scores just came out and they're astounding, Twitter, 30 November 2020.
Tom Whipple, Deepmind finds biology's 'holy grail' with answer to protein problem, The Times (online), 30 November 2020.
In all science editor Tom Whipple wrote six articles on the subject for The Times on the day the news broke. (thread).
Demis Hassabis, "Brief update on some exciting progress on #AlphaFold!" (tweet), via twitter, 18 June 2021

wired.com

"AI Can Help Scientists Find a Covid-19 Vaccine". Wired. ISSN 1059-1028. Retrieved 2020-12-01.

worldcat.org

search.worldcat.org

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