Freyr Sverrisson

1.2k total citations · 1 hit paper
5 papers, 515 citations indexed

About

Freyr Sverrisson is a scholar working on Molecular Biology, Computational Theory and Mathematics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Freyr Sverrisson has authored 5 papers receiving a total of 515 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Molecular Biology, 2 papers in Computational Theory and Mathematics and 1 paper in Computer Vision and Pattern Recognition. Recurrent topics in Freyr Sverrisson's work include Protein Structure and Dynamics (3 papers), Computational Drug Discovery Methods (2 papers) and vaccines and immunoinformatics approaches (1 paper). Freyr Sverrisson is often cited by papers focused on Protein Structure and Dynamics (3 papers), Computational Drug Discovery Methods (2 papers) and vaccines and immunoinformatics approaches (1 paper). Freyr Sverrisson collaborates with scholars based in Switzerland, United Kingdom and Italy. Freyr Sverrisson's co-authors include Bruno E. Correia, Michael M. Bronstein, Federico Monti, Emanuele Rodolà, Davide Boscaini, Pablo Gaínza, Jean Feydy, Stéphane Rosset, Jaume Bonet and Andreas Scheck and has published in prestigious journals such as Nature Methods, PLoS Computational Biology and SPIRE - Sciences Po Institutional REpository.

In The Last Decade

Freyr Sverrisson

4 papers receiving 501 citations

Hit Papers

Deciphering interaction fingerprints from protein molecul... 2019 2026 2021 2023 2019 100 200 300 400
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.

  1. Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning
    2019 449 citations Pablo Gaínza, Freyr Sverrisson et al. Nature Methods profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Freyr Sverrisson Switzerland 3 403 194 119 55 33 5 515
Yanyi Chu China 15 696 1.7× 383 2.0× 140 1.2× 51 0.9× 63 1.9× 20 912
Noelia Ferruz Spain 12 652 1.6× 117 0.6× 131 1.1× 68 1.2× 59 1.8× 20 818
Ghalia Rehawi Germany 3 928 2.3× 188 1.0× 80 0.7× 69 1.3× 61 1.8× 4 1.1k
Hani Neuvirth Israel 8 554 1.4× 181 0.9× 175 1.5× 75 1.4× 54 1.6× 12 716
Raphaël A. G. Chaleil United Kingdom 11 757 1.9× 247 1.3× 188 1.6× 85 1.5× 36 1.1× 19 1.0k
Tomasz Danel Poland 8 142 0.4× 191 1.0× 139 1.2× 26 0.5× 32 1.0× 16 304
Peiran Jiang China 9 369 0.9× 124 0.6× 40 0.3× 114 2.1× 76 2.3× 12 583
Masahito Ohue Japan 17 571 1.4× 253 1.3× 100 0.8× 36 0.7× 17 0.5× 63 744
David La United States 14 571 1.4× 131 0.7× 169 1.4× 67 1.2× 10 0.3× 19 720
Kristy A. Carpenter United States 7 199 0.5× 172 0.9× 59 0.5× 21 0.4× 28 0.8× 13 349

Countries citing papers authored by Freyr Sverrisson

Since Specialization
Citations

This map shows the geographic impact of Freyr Sverrisson'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 Freyr Sverrisson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Freyr Sverrisson more than expected).

Fields of papers citing papers by Freyr Sverrisson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Freyr Sverrisson. 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 Freyr Sverrisson. The network helps show where Freyr Sverrisson may publish in the future.

Co-authorship network of co-authors of Freyr Sverrisson

This figure shows the co-authorship network connecting the top 25 collaborators of Freyr Sverrisson. A scholar is included among the top collaborators of Freyr Sverrisson 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 Freyr Sverrisson. Freyr Sverrisson is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

5 of 5 papers shown
1.
Sverrisson, Freyr, Mehmet Akdel, Jean Feydy, et al.. (2023). DiffMaSIF: Surface-based Protein-Protein Docking with Diffusion Models. SPIRE - Sciences Po Institutional REpository.
2.
Sverrisson, Freyr, Jean Feydy, Bruno E. Correia, & Michael M. Bronstein. (2021). Fast end-to-end learning on protein surfaces. SPIRE - Sciences Po Institutional REpository. 15267–15276. 38 indexed citations
3.
Gaínza, Pablo, Freyr Sverrisson, Federico Monti, et al.. (2019). Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning. Nature Methods. 17(2). 184–192. 449 indexed citations breakdown →
4.
Gaínza, Pablo, Freyr Sverrisson, Federico Monti, et al.. (2019). MaSIF - Deciphering interaction fingerprints from protein molecular surfaces. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations
5.
Bonet, Jaume, Sarah Wehrle, Che Yang, et al.. (2018). Rosetta FunFolDes – A general framework for the computational design of functional proteins. PLoS Computational Biology. 14(11). e1006623–e1006623. 27 indexed citations

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.

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