AlphaFold (Chinese Wikipedia)

Gopalan, Shreya; Narayanan, Sundar. Hallucinations in AlphaFold3 for Intrinsically Disordered Proteins with disorder in Biological Process Residues. 2025. arXiv:2510.15939  （英语）.
McBride, John M.; et al. AlphaFold2 Can Predict Single-Mutation Effects. Physical Review Letters. 2023-11-20, 131 (21): 218401. Bibcode:2023PhRvL.131u8401M. ISSN 0031-9007. PMID 38072605. arXiv:2204.06860 . doi:10.1103/PhysRevLett.131.218401 （英语）.
McBride, John M.; Tlusty, Tsvi. AI-Predicted Protein Deformation Encodes Energy Landscape Perturbation. Physical Review Letters. 2024-08-26, 133 (9): 098401. Bibcode:2024PhRvL.133i8401M. ISSN 0031-9007. PMID 39270162. arXiv:2311.18222 . doi:10.1103/PhysRevLett.133.098401 （英语）.

blog.google (Global: 4,904^th place; Chinese: 2,867^th place)

AlphaFold 3 predicts the structure and interactions of all of life's molecules. Google. 2024-05-08 [2024-05-09]. （原始内容存档于2024-05-09）（英语）.

bloomberg.com (Global: 118^th place; Chinese: 182^nd place)

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

chemistryworld.com (Global: low place; Chinese: low place)

Ball, Phillip. Behind the screens of AlphaFold. Chemistry World. 9 December 2020 [10 December 2020]. （原始内容存档于2021-08-15）（英语）.

cnbc.com (Global: 303^rd place; Chinese: 415^th place)

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

cnn.com (Global: 36^th place; Chinese: 66^th place)

edition.cnn.com

Edwards, Christian; Hunt, Katie. Scientists who used AI to 'crack the code' of almost all proteins win Nobel Prize in chemistry. CNN. 9 October 2024 [9 October 2024]. （原始内容存档于2024-10-10）（英语）.

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

AlphaFold. Deepmind. [2020-11-30]. （原始内容存档于2021-01-19）（英语）.
AlphaFold: Using AI for scientific discovery. Deepmind. 15 January 2020 [2020-11-30]. （原始内容存档于2022-03-07）（英语）.
AlphaFold: a solution to a 50-year-old grand challenge in biology. Deepmind. 30 November 2020 [30 November 2020]. （原始内容存档于2020-11-30）（英语）.
Putting the power of AlphaFold into the world's hands. Deepmind. 22 July 2022 [2021-07-24]. （原始内容存档于2021-07-24）（英语）.
Computational predictions of protein structures associated with COVID-19. Deepmind. 4 August 2020 [2020-12-01]. （原始内容存档于2022-03-25）（英语）.

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

Enabling high-accuracy protein structure prediction at the proteome scale. Google DeepMind. 2021-07-22 [2026-07-10] （英语）.

doi.org (Global: 2^nd place; Chinese: 5^th place)

Callaway, Ewen. 'It will change everything': DeepMind's AI makes gigantic leap in solving protein structures. Nature. 2020-11-30, 588 (7837): 203–204. Bibcode:2020Natur.588..203C. PMID 33257889. doi:10.1038/d41586-020-03348-4  （英语）.
Abramson, Josh; et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature. 2024-05-08, 630 (8016): 493–500. Bibcode:2024Natur.630..493A. ISSN 1476-4687. PMC 11168924 . PMID 38718835. doi:10.1038/s41586-024-07487-w  （英语）.
Jumper, John; et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021-07-15, 596 (7873): 583–589. Bibcode:2021Natur.596..583J. PMC 8371605 . PMID 34265844. doi:10.1038/s41586-021-03819-2  （英语）.
Dill, Ken A.; Ozkan, S. Banu; Shell, M. Scott; Weikl, Thomas R. The Protein Folding Problem. Annual Review of Biophysics. 2008, 37: 289–316. PMC 2443096 . PMID 18573083. doi:10.1146/annurev.biophys.37.092707.153558 （英语）.
Torrisi, Mirko; Pollastri, Gianluca; Le, Quan. Deep learning methods in protein structure prediction. Computational and Structural Biotechnology Journal. 2020, 18: 1301–1310. PMC 7305407 . PMID 32612753. doi:10.1016/j.csbj.2019.12.011 （英语）.
AlQuraishi, Mohammed. AlphaFold at CASP13 (PDF). Bioinformatics. 2019, 35 (22): 4862–4865 [2022-07-29]. doi:10.1093/bioinformatics/btz422. （原始内容存档 (PDF)于2021-11-22）（英语）.
Mirdita, Milot; Schütze, Konstantin; Moriwaki, Yoshitaka; Heo, Lim; Ovchinnikov, Sergey; Steinegger, Martin. ColabFold: Making protein folding accessible to all. Nature Methods. 2022-05-30, 19 (6): 679–682. PMC 9184281 . PMID 35637307. doi:10.1038/s41592-022-01488-1  （英语）.
Callaway, Ewen. What's next for AlphaFold and the AI protein-folding revolution. Nature. 13 April 2022, 604 (7905): 234–238. Bibcode:2022Natur.604..234C. PMID 35418629. S2CID 248156195. doi:10.1038/d41586-022-00997-5  （英语）.
Singh, Arunima. Deep learning 3D structures. Nature Methods. 2020, 17 (3): 249. ISSN 1548-7105. PMID 32132733. S2CID 212403708. doi:10.1038/s41592-020-0779-y  （英语）.
Zheng, Wei; et al. Deep-learning contact-map guided protein structure prediction in CASP13. Proteins: Structure, Function, and Bioinformatics. 2019, 87 (12): 1149–1164. PMID 31365149. doi:10.1002/prot.25792 （英语）.
Hou, Jie; Wu, Tianqi; Cao, Renzhi; Cheng, Jianlin. Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13. Proteins: Structure, Function, and Bioinformatics (Wiley). 2019-04-25, 87 (12): 1165–1178. ISSN 0887-3585. PMC 6800999 . PMID 30985027. bioRxiv 10.1101/552422 . doi:10.1002/prot.25697 （英语）.
Callaway, Ewen. After AlphaFold: protein-folding contest seeks next big breakthrough. Nature. 2022-12-13, 613 (7942): 13–14. PMID 36513827. S2CID 254660427. doi:10.1038/d41586-022-04438-1  （英语）.
Yuan, Rongqing; et al. CASP16 Protein Monomer Structure Prediction Assessment. 2025. bioRxiv 10.1101/2025.05.29.656942  （英语）.
ByteDance AML AI4Science Team. Protenix - Advancing Structure Prediction Through a Comprehensive AlphaFold3 Reproduction. 2025-01-11. bioRxiv 10.1101/2025.01.08.631967  （英语）.
Naddaf, Miryam. Open-source protein structure AI aims to match AlphaFold. Nature. 28 October 2025. PMID 41152525. doi:10.1038/d41586-025-03546-y （英语）.
Lin, Z; et al. Evolutionary-scale prediction of atomic-level protein structure with a language model.. Science. 17 March 2023, 379 (6637): 1123–1130. Bibcode:2023Sci...379.1123L. PMID 36927031. doi:10.1126/science.ade2574  （英语）.
Steinegger, Martin; Söding, Johannes. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nature Biotechnology. 2017, 35 (11): 1026–1028. ISSN 1087-0156. PMID 29035372. doi:10.1038/nbt.3988 （英语）.
Callaway, Ewen. 'The entire protein universe': AI predicts shape of nearly every known protein. Nature. 2022-07-28, 608 (7921): 15–16. Bibcode:2022Natur.608...15C. PMID 35902752. S2CID 251159714. doi:10.1038/d41586-022-02083-2  （英语）.
Lau, Andy M.; et al. Exploring structural diversity across the protein universe with The Encyclopedia of Domains. Science. November 2024, 386 (6721): eadq4946. Bibcode:2024Sci...386q4946L. PMC 7618865 . PMID 39480926. doi:10.1126/science.adq4946  （英语）.
Fleming, Jennifer; et al. AlphaFold Protein Structure Database and 3D-Beacons: New Data and Capabilities. Journal of Molecular Biology. August 2025, 437 (15): 168967. PMID 40133787. doi:10.1016/j.jmb.2025.168967  （英语）.
Hekkelman, Maarten L.; et al. AlphaFill: enriching AlphaFold models with ligands and cofactors. Nature Methods. February 2023, 20 (2): 205–213. Bibcode:2023NatCB..20..205H. PMC 9911346 . PMID 36424442. doi:10.1038/s41592-022-01685-y  （英语）.
Rosignoli, Serena; et al. An outlook on structural biology after AlphaFold: tools, limits and perspectives. FEBS Open Bio. February 2025, 15 (2): 202–222. PMC 11788754 . PMID 39313455. doi:10.1002/2211-5463.13902 （英语）.
Špačková, Anna; et al. ChannelsDB 2.0: a comprehensive database of protein tunnels and pores in AlphaFold era. Nucleic Acids Research. 5 January 2024, 52 (D1): D413–D418. PMC 10767935 . PMID 37956324. doi:10.1093/nar/gkad1012 （英语）.
Lang, Benjamin; Babu, M. Madan. AlphaSync is an enhanced AlphaFold structure database synchronized with UniProt. Nature Structural & Molecular Biology. 2025, 32 (12): 2628–2632. PMID 41219581. doi:10.1038/s41594-025-01719-x  （英语）.
Schaeffer, RD; et al. ECOD: Classification of domains in AFDB Swiss-Prot structure predictions.. PLOS Computational Biology. March 2026, 22 (3): e1013431. Bibcode:2026PLSCB..2213431S. PMID 41911251. doi:10.1371/journal.pcbi.1013431  （英语）.
Sommer, Markus J; et al. Structure-guided isoform identification for the human transcriptome. eLife. 15 December 2022, 11: e82556. PMC 9812405 . PMID 36519529. doi:10.7554/eLife.82556  （英语）.
Thornton, Janet M.; Laskowski, Roman A.; Borkakoti, Neera. AlphaFold heralds a data-driven revolution in biology and medicine. Nature Medicine. 2021, 27 (10): 1666–1669. PMID 34635844. doi:10.1038/s41591-021-01533-0 （英语）.
McBride, John M.; et al. AlphaFold2 Can Predict Single-Mutation Effects. Physical Review Letters. 2023-11-20, 131 (21): 218401. Bibcode:2023PhRvL.131u8401M. ISSN 0031-9007. PMID 38072605. arXiv:2204.06860 . doi:10.1103/PhysRevLett.131.218401 （英语）.
McBride, John M.; Tlusty, Tsvi. AI-Predicted Protein Deformation Encodes Energy Landscape Perturbation. Physical Review Letters. 2024-08-26, 133 (9): 098401. Bibcode:2024PhRvL.133i8401M. ISSN 0031-9007. PMID 39270162. arXiv:2311.18222 . doi:10.1103/PhysRevLett.133.098401 （英语）.
Bagdonas, Haroldas; et al. The case for post-predictional modifications in the AlphaFold Protein Structure Database. Nature Structural & Molecular Biology. 2021-10-29, 28 (11): 869–870. ISSN 1545-9985. PMID 34716446. S2CID 240228913. doi:10.1038/s41594-021-00680-9  （英语）.
Škrinjar, Peter. Have protein-ligand cofolding methods moved beyond memorisation?. 2025-08-04. bioRxiv 10.1101/2025.02.03.636309  （英语）.
Masters, Matthew R.; Mahmoud, Amr H.; Lill, Markus A. Investigating whether deep learning models for co-folding learn the physics of protein-ligand interactions. Nature Communications. 2025-10-06, 16 (1): 8854. Bibcode:2025NatCo..16.8854M. ISSN 2041-1723. PMC 12501370 . PMID 41053181. doi:10.1038/s41467-025-63947-5 （英语）.
Dabrowski-Tumanski, Pawel; Stasiak, Andrzej. AlphaFold Blindness to Topological Barriers Affects Its Ability to Correctly Predict Proteins' Topology. Molecules. 7 November 2023, 28 (22): 7462. PMC 10672856 . PMID 38005184. doi:10.3390/molecules28227462  （英语）.
Aubel, Margaux; Eicholt, Lars; Bornberg-Bauer, Erich. Assessing structure and disorder prediction tools for de novo emerged proteins in the age of machine learning. F1000Research. 2023-03-29, 12: 347. PMC 10126731 . PMID 37113259. doi:10.12688/f1000research.130443.1  （英语）.
Michaud, Jennifer M.; Madani, Ali; Fraser, James S. A language model beats alphafold2 on orphans. Nature Biotechnology. November 2022, 40 (11): 1576–1577. PMC 9669189 . PMID 36192635. doi:10.1038/s41587-022-01466-0 （英语）.
Kovalevskiy, Oleg; Mateos-Garcia, Juan; Tunyasuvunakool, Kathryn. AlphaFold two years on: Validation and impact. Proceedings of the National Academy of Sciences. 2024, 121 (34): e2315002121. doi:10.1073/pnas.2315002121 （英语）.

ebi.ac.uk (Global: 4,422^nd place; Chinese: 3,890^th place)

alphafold.ebi.ac.uk

AlphaFold Protein Structure Database. alphafold.ebi.ac.uk. [2021-07-24]. （原始内容存档于2021-07-24）（英语）.
AlphaFold Protein Structure Database. alphafold.ebi.ac.uk. [2021-07-29]. （原始内容存档于2022-07-29）（英语）.
AlphaFold Protein Structure Database. alphafold.ebi.ac.uk. [2021-07-27]. （原始内容存档于2022-07-29）（英语）.
AlphaFold Protein Structure Database. www.alphafold.ebi.ac.uk. [2021-07-24]. （原始内容存档于2022-08-02）（英语）.
What use cases does AlphaFold not support?. AlphaFold Protein Structure Database. [2022-05-15]. （原始内容存档于2022-07-29）（英语）.

economist.com (Global: 331^st place; Chinese: 377^th place)

DeepMind is answering one of biology's biggest challenges. The Economist. 2020-11-30 [2020-11-30]. ISSN 0013-0613. （原始内容存档于2020-12-03）（英语）.

elpais.com (Global: 89^th place; Chinese: 632^nd place)

Domínguez, Nuño. La inteligencia artificial arrasa en uno de los problemas más importantes de la biología. El País. 2020-12-02 [2024-05-12]. （原始内容存档于2022-07-26）（西班牙语）.

fastcompany.com (Global: 1,399^th place; Chinese: 1,933^rd place)

DeepMind's latest AI breakthrough could turbocharge drug discovery. Fast Company. [2023-01-24]. ISSN 1085-9241. （原始内容存档于2023-01-24）（英语）.

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

AlphaFold: Machine learning for protein structure prediction. Foldit. 2020-01-31 [2022-07-29]. （原始内容存档于2022-05-12）（英语）.

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

Bowman, Greg. Protein folding and related problems remain unsolved despite AlphaFold's advance. Folding@home. 2020-12-08 [2022-07-29]. （原始内容存档于2022-07-13）（英语）.

forbes.com (Global: 71^st place; Chinese: 103^rd place)

Knapp, Alex. 2023 Breakthrough Prizes Announced: Deepmind's Protein Folders Awarded $3 Million. Forbes. [2024-05-09]. （原始内容存档于2024-05-09）（英语）.

fortune.com (Global: 647^th place; Chinese: 710^th place)

Kahn, Jeremy. Lessons from DeepMind's breakthrough in protein-folding A.I.. Fortune. 2020-12-01 [2022-07-29]. （原始内容存档于2022-04-08）（英语）.
How DeepMind's new protein-folding A.I. is already helping to combat the coronavirus pandemic.. Fortune. [2020-12-01]. （原始内容存档于2020-11-30）（英语）.

github.com (Global: 380^th place; Chinese: 277^th place)

deepmind/deepmind-research. GitHub. [2020-11-30]. （原始内容存档于2022-02-01）（英语）.
google-deepmind/alphafold3, Google DeepMind, [2025-02-08] （英语）

hms.harvard.edu (Global: low place; Chinese: 9,596^th place)

ccsp.hms.harvard.edu

AlQuraishi, Mohammed. AlphaFold at CASP13 (PDF). Bioinformatics. 2019, 35 (22): 4862–4865 [2022-07-29]. doi:10.1093/bioinformatics/btz422. （原始内容存档 (PDF)于2021-11-22）（英语）.

isomorphiclabs.com (Global: low place; Chinese: low place)

Isomorphic Labs kicks off 2024 with two pharmaceutical collaborations. Isomorphic Labs. 2024-01-08 [2026-07-10] （英语）.

newscientist.com (Global: 1,041^st place; Chinese: 811^th place)

Le Page, Michael. DeepMind's AI biologist can decipher secrets of the machinery of life. New Scientist. 2020-11-30 [2022-07-29]. （原始内容存档于2022-08-02）（英语）.
The predictions of DeepMind's latest AI could revolutionise medicine. New Scientist. 2020-12-02 [2022-07-29]. （原始内容存档于2021-11-07）（英语）.

nih.gov (Global: 5^th place; Chinese: 3^rd place)

ncbi.nlm.nih.gov

Callaway, Ewen. 'It will change everything': DeepMind's AI makes gigantic leap in solving protein structures. Nature. 2020-11-30, 588 (7837): 203–204. Bibcode:2020Natur.588..203C. PMID 33257889. doi:10.1038/d41586-020-03348-4  （英语）.
Abramson, Josh; et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature. 2024-05-08, 630 (8016): 493–500. Bibcode:2024Natur.630..493A. ISSN 1476-4687. PMC 11168924 . PMID 38718835. doi:10.1038/s41586-024-07487-w  （英语）.
Jumper, John; et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021-07-15, 596 (7873): 583–589. Bibcode:2021Natur.596..583J. PMC 8371605 . PMID 34265844. doi:10.1038/s41586-021-03819-2  （英语）.
Dill, Ken A.; Ozkan, S. Banu; Shell, M. Scott; Weikl, Thomas R. The Protein Folding Problem. Annual Review of Biophysics. 2008, 37: 289–316. PMC 2443096 . PMID 18573083. doi:10.1146/annurev.biophys.37.092707.153558 （英语）.
Torrisi, Mirko; Pollastri, Gianluca; Le, Quan. Deep learning methods in protein structure prediction. Computational and Structural Biotechnology Journal. 2020, 18: 1301–1310. PMC 7305407 . PMID 32612753. doi:10.1016/j.csbj.2019.12.011 （英语）.
Mirdita, Milot; Schütze, Konstantin; Moriwaki, Yoshitaka; Heo, Lim; Ovchinnikov, Sergey; Steinegger, Martin. ColabFold: Making protein folding accessible to all. Nature Methods. 2022-05-30, 19 (6): 679–682. PMC 9184281 . PMID 35637307. doi:10.1038/s41592-022-01488-1  （英语）.
Callaway, Ewen. What's next for AlphaFold and the AI protein-folding revolution. Nature. 13 April 2022, 604 (7905): 234–238. Bibcode:2022Natur.604..234C. PMID 35418629. S2CID 248156195. doi:10.1038/d41586-022-00997-5  （英语）.
Singh, Arunima. Deep learning 3D structures. Nature Methods. 2020, 17 (3): 249. ISSN 1548-7105. PMID 32132733. S2CID 212403708. doi:10.1038/s41592-020-0779-y  （英语）.
Zheng, Wei; et al. Deep-learning contact-map guided protein structure prediction in CASP13. Proteins: Structure, Function, and Bioinformatics. 2019, 87 (12): 1149–1164. PMID 31365149. doi:10.1002/prot.25792 （英语）.
Hou, Jie; Wu, Tianqi; Cao, Renzhi; Cheng, Jianlin. Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13. Proteins: Structure, Function, and Bioinformatics (Wiley). 2019-04-25, 87 (12): 1165–1178. ISSN 0887-3585. PMC 6800999 . PMID 30985027. bioRxiv 10.1101/552422 . doi:10.1002/prot.25697 （英语）.
Callaway, Ewen. After AlphaFold: protein-folding contest seeks next big breakthrough. Nature. 2022-12-13, 613 (7942): 13–14. PMID 36513827. S2CID 254660427. doi:10.1038/d41586-022-04438-1  （英语）.
Naddaf, Miryam. Open-source protein structure AI aims to match AlphaFold. Nature. 28 October 2025. PMID 41152525. doi:10.1038/d41586-025-03546-y （英语）.
Lin, Z; et al. Evolutionary-scale prediction of atomic-level protein structure with a language model.. Science. 17 March 2023, 379 (6637): 1123–1130. Bibcode:2023Sci...379.1123L. PMID 36927031. doi:10.1126/science.ade2574  （英语）.
Steinegger, Martin; Söding, Johannes. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nature Biotechnology. 2017, 35 (11): 1026–1028. ISSN 1087-0156. PMID 29035372. doi:10.1038/nbt.3988 （英语）.
Callaway, Ewen. 'The entire protein universe': AI predicts shape of nearly every known protein. Nature. 2022-07-28, 608 (7921): 15–16. Bibcode:2022Natur.608...15C. PMID 35902752. S2CID 251159714. doi:10.1038/d41586-022-02083-2  （英语）.
Lau, Andy M.; et al. Exploring structural diversity across the protein universe with The Encyclopedia of Domains. Science. November 2024, 386 (6721): eadq4946. Bibcode:2024Sci...386q4946L. PMC 7618865 . PMID 39480926. doi:10.1126/science.adq4946  （英语）.
Fleming, Jennifer; et al. AlphaFold Protein Structure Database and 3D-Beacons: New Data and Capabilities. Journal of Molecular Biology. August 2025, 437 (15): 168967. PMID 40133787. doi:10.1016/j.jmb.2025.168967  （英语）.
Hekkelman, Maarten L.; et al. AlphaFill: enriching AlphaFold models with ligands and cofactors. Nature Methods. February 2023, 20 (2): 205–213. Bibcode:2023NatCB..20..205H. PMC 9911346 . PMID 36424442. doi:10.1038/s41592-022-01685-y  （英语）.
Rosignoli, Serena; et al. An outlook on structural biology after AlphaFold: tools, limits and perspectives. FEBS Open Bio. February 2025, 15 (2): 202–222. PMC 11788754 . PMID 39313455. doi:10.1002/2211-5463.13902 （英语）.
Špačková, Anna; et al. ChannelsDB 2.0: a comprehensive database of protein tunnels and pores in AlphaFold era. Nucleic Acids Research. 5 January 2024, 52 (D1): D413–D418. PMC 10767935 . PMID 37956324. doi:10.1093/nar/gkad1012 （英语）.
Lang, Benjamin; Babu, M. Madan. AlphaSync is an enhanced AlphaFold structure database synchronized with UniProt. Nature Structural & Molecular Biology. 2025, 32 (12): 2628–2632. PMID 41219581. doi:10.1038/s41594-025-01719-x  （英语）.
Schaeffer, RD; et al. ECOD: Classification of domains in AFDB Swiss-Prot structure predictions.. PLOS Computational Biology. March 2026, 22 (3): e1013431. Bibcode:2026PLSCB..2213431S. PMID 41911251. doi:10.1371/journal.pcbi.1013431  （英语）.
Sommer, Markus J; et al. Structure-guided isoform identification for the human transcriptome. eLife. 15 December 2022, 11: e82556. PMC 9812405 . PMID 36519529. doi:10.7554/eLife.82556  （英语）.
Thornton, Janet M.; Laskowski, Roman A.; Borkakoti, Neera. AlphaFold heralds a data-driven revolution in biology and medicine. Nature Medicine. 2021, 27 (10): 1666–1669. PMID 34635844. doi:10.1038/s41591-021-01533-0 （英语）.
McBride, John M.; et al. AlphaFold2 Can Predict Single-Mutation Effects. Physical Review Letters. 2023-11-20, 131 (21): 218401. Bibcode:2023PhRvL.131u8401M. ISSN 0031-9007. PMID 38072605. arXiv:2204.06860 . doi:10.1103/PhysRevLett.131.218401 （英语）.
McBride, John M.; Tlusty, Tsvi. AI-Predicted Protein Deformation Encodes Energy Landscape Perturbation. Physical Review Letters. 2024-08-26, 133 (9): 098401. Bibcode:2024PhRvL.133i8401M. ISSN 0031-9007. PMID 39270162. arXiv:2311.18222 . doi:10.1103/PhysRevLett.133.098401 （英语）.
Bagdonas, Haroldas; et al. The case for post-predictional modifications in the AlphaFold Protein Structure Database. Nature Structural & Molecular Biology. 2021-10-29, 28 (11): 869–870. ISSN 1545-9985. PMID 34716446. S2CID 240228913. doi:10.1038/s41594-021-00680-9  （英语）.
Masters, Matthew R.; Mahmoud, Amr H.; Lill, Markus A. Investigating whether deep learning models for co-folding learn the physics of protein-ligand interactions. Nature Communications. 2025-10-06, 16 (1): 8854. Bibcode:2025NatCo..16.8854M. ISSN 2041-1723. PMC 12501370 . PMID 41053181. doi:10.1038/s41467-025-63947-5 （英语）.
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AlphaFold (Chinese Wikipedia)

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nih.gov (Global: 5^th place; Chinese: 3^rd place)

nobelprize.org (Global: 265^th place; Chinese: 221^st place)

nytimes.com (Global: 7^th place; Chinese: 21^st place)

oup.com (Global: 292^nd place; Chinese: 319^th place)

people.com.cn (Global: 295^th place; Chinese: 17^th place)

scholar.google.com (Global: 1,418^th place; Chinese: 2,581^st place)

science.org (Global: 1,007^th place; Chinese: 1,086^th place)

sciencenet.cn (Global: 8,155^th place; Chinese: 474^th place)

semanticscholar.org (Global: 15^th place; Chinese: 141^st place)

technologyreview.com (Global: 2,515^th place; Chinese: 1,750^th place)

telegraph.co.uk (Global: 40^th place; Chinese: 93^rd place)

theguardian.com (Global: 16^th place; Chinese: 48^th place)

thetimes.com (Global: 221^st place; Chinese: 1,552^nd place)

twitter.com (Global: 43^rd place; Chinese: 45^th place)

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

wired.com (Global: 254^th place; Chinese: 350^th place)

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