Cited by 505Protein Structure and DynamicsEnzyme Structure and FunctionMachine Learning in Bioinformatics

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‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures

Callaway, Ewen·2020·Nature

DOI 10.1038/d41586-020-03348-4

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“We have therefore referred to the Nsp2 model generated by AlphaFold2 from DeepMind (29).”
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Flower, T. G., & Hurley, J. H. (2021) · Protein Science · doi:10.1002/pro.4050 · Cited by 49
“4 out of 100.(21,22) Our group recently determined the crystal structure of ORF8 at 2.”
Background
Classification Model for the Second Extracellular Loop of Class A GPCRs
Nicoli, A., Dunkel, A., Toni Giorgino, A., de Graaf, C., & Pizio, A. D. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c01056 · Cited by 55
Efficient and easible biocatalysts: Strategies for enzyme improvement. A review
Albayati, S. H., Nezhad, N. G., Taki, A. G., & Rahman, R. N. Z. R. A. (2024) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2024.133978 · Cited by 59
Chemistry 2030: A Roadmap for a New Decade
García‐Martínez, J. (2021) · Angewandte Chemie International Edition · doi:10.1002/anie.202014779 · Cited by 57
Artificial intelligence guided conformational mining of intrinsically disordered proteins
Gupta, A., Dey, S., Hicks, A., & Zhou, H.-X. (2022) · Communications Biology · doi:10.1038/s42003-022-03562-y · Cited by 56
Human-AI collaboration by design
Song, B., Zhu, Q., & Luo, J. (2024) · Proceedings of the Design Society · doi:10.1017/pds.2024.227 · Cited by 78
Overview of AlphaFold2 and breakthroughs in overcoming its limitations
Wang, L., Wen, Z., Liu, S.-W., Zhang, L., Finley, C., Lee, H., & Fan, H. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108620 · Cited by 56
Protein–Protein Docking: Past, Present, and Future
Sunny, S., & Jayaraj, P. B. (2021) · The Protein Journal · doi:10.1007/s10930-021-10031-8 · Cited by 51
“The groundbreaking solution to this hard problem was proposed by DeepMind [114].”
Perspectives on High-Throughput Ligand/Protein Docking With Martini MD Simulations
Souza, P. C. T., Limongelli, V., Wu, S., Marrink, ‪. J., & Monticelli, L. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.657222 · Cited by 52
“…applications have similar pitfalls, with the current approaches reasonably successful in predicting protein structures (Hutson, 2019; Callaway, 2020) and rigid-body protein-protein interactions (Siebenmorgen and Zacharias, 2020) but with limitations in the design of conformational…”
Method
AlphaFold predicts the most complex protein knot and composite protein knots
Brems, M. A., Runkel, R., Yeates, T. O., & Virnau, P. (2022) · Protein Science · doi:10.1002/pro.4380 · Cited by 49
“Recently, the artificial intelligence (AI) system AlphaFold developed by Google’s DeepMind dominated the Critical Assessment of Techniques for Protein Structure Prediction (CASP) twice.(1) AlphaFold 2, the version under consideration here, is a deep learning system that incorporates training procedures based on the ev…”
Background
Students’ preferences with university teaching practices: analysis of testimonials with artificial intelligence
Álvarez, C. Á., & Falcón, S. (2023) · Educational Technology Research and Development · doi:10.1007/s11423-023-10239-8 · Cited by 67
“This is especially notable in biology, where the development of AlphaFold 2, an AI-based tool capable of predicting the three-dimensional structure of proteins, has been a milestone (Callaway, 2020; Jumper et al., 2021).”
Background
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Higgins, M. K. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167093 · Cited by 48
“Nevertheless, the CASP competitions are stringent, rigorous and robust peer review processes in their own right(10) and AlphaFold 2 was by some distance the best prediction method yet to test its arm in the competition.(9) In particular, its performance was strongest in the challenging ‘free model’ category in CASP.”
Method
From Lock-In to Transformation: A Path-Centric Theory of Emerging Technology and Organizing
Pentland, B. T., Yoo, Y., Recker, J., & Kim, I. (2021) · Organization Science · doi:10.1287/orsc.2021.1543 · Cited by 47
“The new technology is not just radically shortening the vaccine development process; fundamentally new patterns of activity for drug discovery are emerging (Cui et al. 2020, Chaudhari et al. 2020, Lin, Li, and Lin 2020, Callaway 2020).”
Background
Advanced AI and ML frameworks for transforming drug discovery and optimization: With innovative insights in polypharmacology, drug repurposing, combination therapy and nanomedicine
Ambreen, S., Umar, M., Noor, A., Jain, H., & Ali, R. (2024) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2024.117164 · Cited by 51
Could AlphaFold revolutionize chemical therapeutics?
Tong, A., Burch, J. D., McKay, D. B., Bustamante, C., Crackower, M. A., & Wu, H. (2021) · Nature Structural & Molecular Biology · doi:10.1038/s41594-021-00670-x · Cited by 43
Grand Challenges in Bioinformatics Data Visualization
O’Donoghue, S. I. (2021) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2021.669186 · Cited by 51
Revealing RNA virus diversity and evolution in unicellular algae transcriptomes
Charon, J., Murray, S. A., & Holmes, E. C. (2021) · Virus Evolution · doi:10.1093/ve/veab070 · Cited by 42
Overexpression and kinetic analysis of Ideonella sakaiensis PETase for polyethylene terephthalate (PET) degradation
Aer, L., Jiang, Q., Gul, I., Qi, Z., Feng, J., & Tang, L. (2022) · Environmental Research · doi:10.1016/j.envres.2022.113472 · Cited by 46
Recent advances and challenges in experiment-oriented polymer informatics
Hatakeyama‐Sato, K. (2022) · Polymer Journal · doi:10.1038/s41428-022-00734-9 · Cited by 46

Authors: Callaway, E.
Corresponding author: Callaway, Ewen
Venue: Nature
Publisher: Springer Science and Business Media LLC
Published: 2020-11-30
Volume: 588
Issue: 7837
Pages: 203-204
ISSN: 0028-0836 (ISSN-L)
1476-4687
DOI: 10.1038/d41586-020-03348-4
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
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
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

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‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures

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