John Jumper

82.3k total citations · 7 hit papers
24 papers, 5.7k citations indexed

About

John Jumper is a scholar working on Molecular Biology, Materials Chemistry and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, John Jumper has authored 24 papers receiving a total of 5.7k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Molecular Biology, 10 papers in Materials Chemistry and 4 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in John Jumper's work include Protein Structure and Dynamics (12 papers), Enzyme Structure and Function (9 papers) and RNA and protein synthesis mechanisms (5 papers). John Jumper is often cited by papers focused on Protein Structure and Dynamics (12 papers), Enzyme Structure and Function (9 papers) and RNA and protein synthesis mechanisms (5 papers). John Jumper collaborates with scholars based in United States, United Kingdom and Sweden. John Jumper's co-authors include Demis Hassabis, Pushmeet Kohli, David E. Shaw, Michael P. Eastwood, Andrew Senior, Ron O. Dror, Yibing Shan, Stefano Piana, John K. Salmon and Paul Maragakis and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

John Jumper

23 papers receiving 5.6k citations

Hit Papers

5 papers align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Improved protein structure prediction using potentials from deep learning

2020 2.0k citations Andrew Senior, John Jumper et al. Nature profile →
Atomic-Level Characterization of the Structural Dynamics of Proteins

2010 1.4k citations David E. Shaw, Paul Maragakis et al. Science profile →
Accurate proteome-wide missense variant effect prediction with AlphaMissense

2023 692 citations Jun Cheng, Guido Novati et al. Science profile →
Effective gene expression prediction from sequence by integrating long-range interactions

2021 486 citations Žiga Avsec, Vikram Agarwal et al. Nature Methods profile →
Oncogenic Mutations Counteract Intrinsic Disorder in the EGFR Kinase and Promote Receptor Dimerization

2012 276 citations Yibing Shan, Michael P. Eastwood et al. Cell profile →
Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13)

2019 208 citations Andrew Senior, John Jumper et al. profile →
Protein structure predictions to atomic accuracy with AlphaFold

2022 157 citations John Jumper, Demis Hassabis Nature Methods profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
John Jumper United States	15	4.1k	1.3k	609	526	415	24	5.7k
Taehoon Kim South Korea	16	5.5k 1.3×	596 0.5×	555 0.9×	439 0.8×	474 1.1×	58	7.8k
Patrice Koehl United States	35	3.8k 0.9×	1.4k 1.1×	337 0.6×	337 0.6×	331 0.8×	127	5.1k
Judith Klein‐Seetharaman United States	46	4.6k 1.1×	1.1k 0.9×	448 0.7×	223 0.4×	385 0.9×	174	6.7k
Reinhard Schneider Germany	43	7.0k 1.7×	1.3k 1.0×	915 1.5×	718 1.4×	455 1.1×	196	10.5k
John Westbrook United States	39	7.0k 1.7×	2.3k 1.8×	1.4k 2.3×	392 0.7×	616 1.5×	103	9.6k
M. Michael Gromiha India	51	8.4k 2.0×	2.1k 1.7×	1.1k 1.8×	719 1.4×	482 1.2×	339	10.0k
Rhiju Das United States	56	10.2k 2.5×	1.7k 1.4×	392 0.6×	712 1.4×	536 1.3×	152	11.9k
Leslie A. Kuhn United States	33	3.2k 0.8×	908 0.7×	919 1.5×	175 0.3×	186 0.4×	73	4.8k
Sanguk Kim South Korea	46	3.9k 1.0×	630 0.5×	191 0.3×	349 0.7×	541 1.3×	177	7.0k
Hui Lü China	43	5.9k 1.4×	1.2k 1.0×	509 0.8×	629 1.2×	237 0.6×	314	9.7k

Countries citing papers authored by John Jumper

Since Specialization

Citations

This map shows the geographic impact of John Jumper's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by John Jumper with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Jumper more than expected).

Fields of papers citing papers by John Jumper

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John Jumper. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by John Jumper. The network helps show where John Jumper may publish in the future.

Co-authorship network of co-authors of John Jumper

This figure shows the co-authorship network connecting the top 25 collaborators of John Jumper. A scholar is included among the top collaborators of John Jumper based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with John Jumper. John Jumper is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Cheng, Jun, Guido Novati, Taylor Applebaum, et al.. (2023). Predictions for AlphaMissense. Zenodo (CERN European Organization for Nuclear Research). 3 indexed citations

Cheng, Jun, Guido Novati, Clare Bycroft, et al.. (2023). Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science. 381(6664). eadg7492–eadg7492. 692 indexed citations breakdown →

Jumper, John & Demis Hassabis. (2023). The Protein Structure Prediction Revolution and Its Implications for Medicine. JAMA. 330(15). 1425–1425. 7 indexed citations

Borukhov, Sergei, et al.. (2022). Structural basis of template strand deoxyuridine promoter recognition by a viral RNA polymerase. Nature Communications. 13(1). 3526–3526. 4 indexed citations

Stsiapanava, Alena, Chenrui Xu, Ling Han, et al.. (2022). Structure of the decoy module of human glycoprotein 2 and uromodulin and its interaction with bacterial adhesin FimH. Nature Structural & Molecular Biology. 29(3). 190–193. 21 indexed citations

Jumper, John & Demis Hassabis. (2022). Protein structure predictions to atomic accuracy with AlphaFold. Nature Methods. 19(1). 11–12. 157 indexed citations breakdown →

Moi, David, Xiaohui Li, Clari Valansi, et al.. (2022). Discovery of archaeal fusexins homologous to eukaryotic HAP2/GCS1 gamete fusion proteins. Nature Communications. 13(1). 3880–3880. 24 indexed citations

Judge, Russell A., Kathryn Tunyasuvunakool, Rinku Jain, et al.. (2022). Structure of the PAPP-ABP5 complex reveals mechanism of substrate recognition. Nature Communications. 13(1). 5500–5500. 11 indexed citations

Avsec, Žiga, Vikram Agarwal, Daniel Visentin, et al.. (2021). Effective gene expression prediction from sequence by integrating long-range interactions. Nature Methods. 18(10). 1196–1203. 486 indexed citations breakdown →

10.

Senior, Andrew, John Jumper, James Kirkpatrick, et al.. (2020). Improved protein structure prediction using potentials from deep learning. Nature. 577(7792). 706–710. 2026 indexed citations breakdown →

11.

Jumper, John, et al.. (2019). On the Interpretation of Force-Induced Unfolding Studies of Membrane Proteins Using Fast Simulations. Biophysical Journal. 117(8). 1429–1441. 9 indexed citations

12.

Jumper, John, et al.. (2018). A Membrane Burial Potential with H-Bonds and Applications to Curved Membranes and Fast Simulations. Biophysical Journal. 115(10). 1872–1884. 7 indexed citations

13.

Jumper, John, et al.. (2018). Trajectory-based training enables protein simulations with accurate folding and Boltzmann ensembles in cpu-hours. PLoS Computational Biology. 14(12). e1006578–e1006578. 29 indexed citations

14.

Jumper, John, et al.. (2018). Accurate calculation of side chain packing and free energy with applications to protein molecular dynamics. PLoS Computational Biology. 14(12). e1006342–e1006342. 29 indexed citations

15.

Riback, Joshua A., Micayla A. Bowman, Adam M. Zmyslowski, et al.. (2017). Innovative scattering analysis shows that hydrophobic disordered proteins are expanded in water. Science. 358(6360). 238–241. 155 indexed citations

16.

Jumper, John, et al.. (2016). Including H-Bonding in Depth-Dependent Membrane Burial Potentials for Improving Folding Simulations. Biophysical Journal. 110(3). 58a–58a. 1 indexed citations

17.

Zimmerman, John F., Graeme Murray, Yucai Wang, et al.. (2015). Free-Standing Kinked Silicon Nanowires for Probing Inter- and Intracellular Force Dynamics. Nano Letters. 15(8). 5492–5498. 37 indexed citations

18.

Baxa, Michael C., Esmael J. Haddadian, John Jumper, Karl F. Freed, & Tobin R. Sosnick. (2014). Loss of conformational entropy in protein folding calculated using realistic ensembles and its implications for NMR-based calculations. Proceedings of the National Academy of Sciences. 111(43). 15396–15401. 99 indexed citations

19.

Shan, Yibing, Michael P. Eastwood, Xuewu Zhang, et al.. (2012). Oncogenic Mutations Counteract Intrinsic Disorder in the EGFR Kinase and Promote Receptor Dimerization. Cell. 149(4). 860–870. 276 indexed citations breakdown →

20.

Shaw, David E., Paul Maragakis, Kresten Lindorff‐Larsen, et al.. (2010). Atomic-Level Characterization of the Structural Dynamics of Proteins. Science. 330(6002). 341–346. 1415 indexed citations breakdown →

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact