Leaderboards/Protein Structure and Dynamics

Protein Structure and Dynamics

Last updated July 15, 2026

Rising Stars

Fresh ink, massive impact

Researchers

1
Typhaine Paysan‐Lafosse
European Bioinformatics Institute
2
Matthias Blum
European Bioinformatics Institute
3
Sarah Lewis
Microsoft Research (United Kingdom)
4
Mehmet Akdel
Wageningen University & Research
5
Alexander Kroll
Steinbeis Foundation
6
Barak Yariv
Tel Aviv University
7
Peizhen Bai
University of Sheffield
8
Diego del Alamo
PDL BioPharma (United States)
9
Devlina Chakravarty
National Institutes of Health
10
Magnus Haraldson Høie
Technical University of Denmark

Papers

1
InterPro: the protein sequence classification resource in 2025
Nucleic Acids Research|2024
885 cit.
2
The Pfam protein families database: embracing AI/ML
Nucleic Acids Research|2024
267 cit.
3
Scalable emulation of protein equilibrium ensembles with generative deep learning
Science|2025
221 cit.
4
AlphaFold predictions of fold-switched conformations are driven by structure memorization
Nature Communications|2024
142 cit.
5
DiscoTope-3.0: improved B-cell epitope prediction using inverse folding latent representations
Frontiers in Immunology|2024
122 cit.
6
DLKcat cannot predict meaningful <i>k</i>cat values for mutants and unfamiliar enzymes
Biology Methods and Protocols|2024
30 cit.
7
AntiFold: improved structure-based antibody design using inverse folding
Bioinformatics Advances|2024
30 cit.
8
A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships
PLoS Computational Biology|2024
29 cit.
9
SPOT: A machine learning model that predicts specific substrates for transport proteins
PLoS Biology|2024
21 cit.
10
DeepMolecules: a web server for predicting enzyme and transporter–small molecule interactions
Nucleic Acids Research|2025
11 cit.

Emerging Leaders

Drafting the new consensus

Researchers

1
John Jumper
Google DeepMind (United Kingdom)
2
Max Jaderberg
Industrial Research Institute of Ishikawa
3
Martin Steinegger
Institute of Molecular Biology
4
Sergey Ovchinnikov
Massachusetts Institute of Technology
5
Alexander D. MacKerell
University of Maryland, Baltimore
6
Stefano Forli
Scripps Research Institute
7
Douglas E. V. Pires
Fundação Oswaldo Cruz
8
Alex S. Holehouse
Washington University in St. Louis
9
Marina A. Zhuravleva
Rutgers, The State University of New Jersey
10
Ivan Peran
St. Jude Children's Research Hospital

Papers

1
Accurate structure prediction of biomolecular interactions with AlphaFold 3
Nature|2024
13.8k cit.
2
ColabFold: making protein folding accessible to all
Nature Methods|2022
9.7k cit.
3
Fast and accurate protein structure search with Foldseek
Nature Biotechnology|2023
2.4k cit.
4
Clustering predicted structures at the scale of the known protein universe
Nature|2023
365 cit.
5
Addendum: Accurate structure prediction of biomolecular interactions with AlphaFold 3
Nature|2024
294 cit.
6
Protein structure predictions to atomic accuracy with AlphaFold
Nature Methods|2022
291 cit.
7
State-of-the-Art Estimation of Protein Model Accuracy Using AlphaFold
Physical Review Letters|2022
267 cit.
8
Direct prediction of intrinsically disordered protein conformational properties from sequence
Nature Methods|2024
226 cit.
9
Evaluation of <scp>AlphaFold2</scp> structures as docking targets
Protein Science|2022
106 cit.
10
Protein language models learn evolutionary statistics of interacting sequence motifs
Proceedings of the National Academy of Sciences|2024
104 cit.

Current Senior Leaders

Wrote the book, still writing it

Researchers

1
Demis Hassabis
Google DeepMind (United Kingdom)
2
Martin Karplus
Harvard University
3
Janet M. Thornton
European Bioinformatics Institute
4
David Baker
Howard Hughes Medical Institute
5
Kevin Cowtan
University of York
6
Arthur J. Olson
Scripps Research Institute
7
Thomas E. Ferrin
University of California, San Francisco
8
Ad Bax
National Institutes of Health
9
Erik Lindahl
Linköping University
10
Peter A. Kollman
Rutgers, The State University of New Jersey

Papers

1
UCSF Chimera—A visualization system for exploratory research and analysis
Journal of Computational Chemistry|2004
47.8k cit.
2
Highly accurate protein structure prediction with AlphaFold
Nature|2021
45.2k cit.
3
AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading
Journal of Computational Chemistry|2009
37.1k cit.
4
<i>Coot</i>: model-building tools for molecular graphics
Acta Crystallographica Section D Biological Crystallography|2004
31.4k cit.
5
Human-level control through deep reinforcement learning
Nature|2015
30k cit.
6
Features and development of <i>Coot</i>
Acta Crystallographica Section D Biological Crystallography|2010
29.6k cit.
7
GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers
SoftwareX|2015
26.8k cit.
8
AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility
Journal of Computational Chemistry|2009
25k cit.
9
PROCHECK: a program to check the stereochemical quality of protein structures
Journal of Applied Crystallography|1993
24.6k cit.
10
NMRPipe: A multidimensional spectral processing system based on UNIX pipes
Journal of Biomolecular NMR|1995
16.3k cit.

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