The DOI matches its Crossref record.
‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures
Overview
Integrity signals
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Cites
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Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“AlphaFold has combined numerous deep learning innovations to predict the three-dimensional (3D) structures of proteins at or near experimental scale resolution”
From AlphaFold, Artificial Intelligence (AI), and Allostery (2022) · cites this paper as [3]
Citations
Cited by
Sorted by citations
The trRosetta server for fast and accurate protein structure prediction
Du, Z., Su, H. L., Wang, W., Ye, L., Wei, H., Peng, Z., Anishchenko, I. V., Baker, D. A., & Yang, J. (2021) · Nature Protocols · doi:10.1038/s41596-021-00628-9 · Cited by 737
“The work of AlphaFold2 is regarded as one of the milestones in protein structure prediction and is anticipated to ‘ change everything ’ 10 .”
BackgroundBell, E. L., Finnigan, W., France, S. P., Green, A. P., Hayes, M. A., Hepworth, L. J., Lovelock, S. L., Niikura, H., Osuna, S., Romero, E., et al. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00044-z · Cited by 721
The Ethics of Artificial Intelligence
Floridi, L. (2023) · doi:10.1093/oso/9780198883098.001.0001 · Cited by 682
Artificial intelligence and machine learning for medical imaging: A technology review
Montero, A. M. B., Javaid, U., Valdés, G., Nguyen, D. T. D., Desbordes, P., Macq, B. M. M., Willems, S., Vandewinckele, L., Holmström, M., Löfman, F., et al. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.04.016 · Cited by 510
“A very recent example is the breakthrough of AI in the CASP competition [244].”
Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021
Chaudhry, M. A., & Kazim, E. (2022) · AI and Ethics · doi:10.1007/s43681-021-00074-z · Cited by 425
“These algorithms have achieved breakthrough performance in multiple domains including games [66], healthcare [14] and robotics [34].”
Background
Metadata
- Authors
- Callaway, E.
- Corresponding author
- Callaway, Ewen
- Venue
- Nature
- Published
- 2020-11-30
- Volume
- 588
- Issue
- 7837
- Pages
- 203-204
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W3108943181
- Cited by
- 499
- References
- 0
- Citations by year
- 2026: 15 · 2025: 39 · 2024: 77 · 2023: 87 · 2022: 97 · 2021: 181 · 2020: 2
- Type
- other
- Language
- en
- Open access
- closed
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- DeepMind, AlphaFold, protein structure prediction, protein 3D structure, deep learning, artificial intelligence in biology, protein folding
- MeSH terms
- Deep Learning, Models, Molecular, Protein Conformation, Proteins, Time Factors, Crystallography, X-Ray, Caspases
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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