Highly accurate protein structure prediction with AlphaFold J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, ... nature 596 (7873), 583-589, 2021 | 29529 | 2021 |
AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models M Varadi, S Anyango, M Deshpande, S Nair, C Natassia, G Yordanova, ... Nucleic acids research 50 (D1), D439-D444, 2022 | 5717 | 2022 |
The kinetics human action video dataset W Kay, J Carreira, K Simonyan, B Zhang, C Hillier, S Vijayanarasimhan, ... arXiv preprint arXiv:1705.06950, 2017 | 4784 | 2017 |
Improved protein structure prediction using potentials from deep learning AW Senior, R Evans, J Jumper, J Kirkpatrick, L Sifre, T Green, C Qin, ... Nature 577 (7792), 706-710, 2020 | 3407 | 2020 |
Highly accurate protein structure prediction for the human proteome K Tunyasuvunakool, J Adler, Z Wu, T Green, M Zielinski, A Žídek, ... Nature 596 (7873), 590-596, 2021 | 2393 | 2021 |
Protein complex prediction with AlphaFold-Multimer R Evans, M O’Neill, A Pritzel, N Antropova, A Senior, T Green, A Žídek, ... biorxiv, 2021.10. 04.463034, 2021 | 2366 | 2021 |
Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 2144 | 2023 |
Accurate structure prediction of biomolecular interactions with AlphaFold 3 J Abramson, J Adler, J Dunger, R Evans, T Green, A Pritzel, ... Nature, 1-3, 2024 | 1504 | 2024 |
Human-level performance in 3D multiplayer games with population-based reinforcement learning M Jaderberg, WM Czarnecki, I Dunning, L Marris, G Lever, AG Castaneda, ... Science 364 (6443), 859-865, 2019 | 1018 | 2019 |
Population based training of neural networks M Jaderberg, V Dalibard, S Osindero, WM Czarnecki, J Donahue, ... arXiv preprint arXiv:1711.09846, 2017 | 926 | 2017 |
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ... arXiv preprint arXiv:2403.05530, 2024 | 671* | 2024 |
Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13) AW Senior, R Evans, J Jumper, J Kirkpatrick, L Sifre, T Green, C Qin, ... Proteins: structure, function, and bioinformatics 87 (12), 1141-1148, 2019 | 344 | 2019 |
Applying and improving AlphaFold at CASP14 J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, ... Proteins: Structure, Function, and Bioinformatics 89 (12), 1711-1721, 2021 | 337 | 2021 |
AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences M Varadi, D Bertoni, P Magana, U Paramval, I Pidruchna, ... Nucleic acids research 52 (D1), D368-D375, 2024 | 301 | 2024 |
High accuracy protein structure prediction using deep learning J Jumper, R Evans, A Pritzel, T Green, M Figurnov, K Tunyasuvunakool, ... Fourteenth critical assessment of techniques for protein structure …, 2020 | 231* | 2020 |
De novo structure prediction with deeplearning based scoring R Evans, J Jumper, J Kirkpatrick, L Sifre, T Green, C Qin, A Zidek, ... Annu Rev Biochem 77 (363-382), 6, 2018 | 154 | 2018 |
Elucidation of the Al/Si ordering in Gehlenite Ca2Al2SiO7 by combined 29Si and 27Al NMR spectroscopy/quantum chemical calculations P Florian, E Véron, T Green, JR Yates, D Massiot Chemistry of Materials, 2012 | 116 | 2012 |
Visualization and processing of computed solid-state NMR parameters: MagresView and MagresPython S Sturniolo, TFG Green, RM Hanson, M Zilka, K Refson, P Hodgkinson, ... Solid state nuclear magnetic resonance 78, 64-70, 2016 | 89 | 2016 |
Relativistic nuclear magnetic resonance J-coupling with ultrasoft pseudopotentials and the zeroth-order regular approximation TFG Green, JR Yates J. Chem. Phys., 234106, 2014 | 54 | 2014 |
Computational predictions of protein structures associated with COVID-19 J Jumper, K Tunyasuvunakool, P Kohli, D Hassabis, the AlphaFold team | 38* | 2020 |