Pablo Gaínza

2.5k total citations · 3 hit papers
25 papers, 1.3k citations indexed

About

Pablo Gaínza is a scholar working on Molecular Biology, Radiology, Nuclear Medicine and Imaging and Oncology. According to data from OpenAlex, Pablo Gaínza has authored 25 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Molecular Biology, 5 papers in Radiology, Nuclear Medicine and Imaging and 4 papers in Oncology. Recurrent topics in Pablo Gaínza's work include Protein Structure and Dynamics (10 papers), RNA and protein synthesis mechanisms (7 papers) and Monoclonal and Polyclonal Antibodies Research (5 papers). Pablo Gaínza is often cited by papers focused on Protein Structure and Dynamics (10 papers), RNA and protein synthesis mechanisms (7 papers) and Monoclonal and Polyclonal Antibodies Research (5 papers). Pablo Gaínza collaborates with scholars based in Switzerland, United States and United Kingdom. Pablo Gaínza's co-authors include Bruno E. Correia, Bruce R. Donald, Michael M. Bronstein, Emanuele Rodolà, Freyr Sverrisson, Federico Monti, Davide Boscaini, Kyle E. Roberts, Hunter Nisonoff and Thomas Ryckmans and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Nature Communications.

In The Last Decade

Pablo Gaínza

24 papers receiving 1.3k citations

Hit 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.

Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning

2019 449 citations Pablo Gaínza, Freyr Sverrisson et al. Nature Methods profile →
From Thalidomide to Rational Molecular Glue Design for Targeted Protein Degradation

2023 89 citations Vladimiras Oleinikovas, Pablo Gaínza et al. The Annual Review of Pharmacology and Toxicology profile →
Targeting protein–ligand neosurfaces with a generalizable deep learning tool

2025 19 citations Stephen Buckley, Arne Schneuing et al. Nature profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Pablo Gaínza Switzerland	14	994	259	258	183	157	25	1.3k
Mateusz Kurciński Poland	16	1.1k 1.1×	256 1.0×	186 0.7×	219 1.2×	124 0.8×	28	1.3k
Gordon Lemmon United States	12	949 1.0×	165 0.6×	156 0.6×	218 1.2×	99 0.6×	16	1.3k
Barak Raveh Israel	18	1.8k 1.8×	288 1.1×	273 1.1×	161 0.9×	268 1.7×	26	2.2k
Oliver Koch Germany	21	1.4k 1.4×	386 1.5×	132 0.5×	186 1.0×	205 1.3×	89	1.9k
Duncan E. Scott United Kingdom	13	1.3k 1.3×	287 1.1×	165 0.6×	124 0.7×	278 1.8×	18	1.8k
Rebecca F. Alford United States	6	990 1.0×	124 0.5×	152 0.6×	230 1.3×	72 0.5×	12	1.2k
Brian Jiménez‐García Spain	19	1.6k 1.6×	389 1.5×	290 1.1×	302 1.7×	121 0.8×	30	1.9k
Sergey Lyskov United States	14	1.6k 1.6×	226 0.9×	435 1.7×	323 1.8×	138 0.9×	19	2.1k
Jeliazko R. Jeliazkov United States	13	1.3k 1.3×	137 0.5×	496 1.9×	239 1.3×	94 0.6×	21	1.6k
Gaoqi Weng China	16	1.2k 1.2×	435 1.7×	155 0.6×	133 0.7×	229 1.5×	25	1.5k

Countries citing papers authored by Pablo Gaínza

Since Specialization

Citations

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

Fields of papers citing papers by Pablo Gaínza

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pablo Gaínza

This figure shows the co-authorship network connecting the top 25 collaborators of Pablo Gaínza. A scholar is included among the top collaborators of Pablo Gaínza 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 Pablo Gaínza. Pablo Gaínza 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

Langousis, Gerasimos, Pablo Gaínza, Moritz Hunkeler, et al.. (2025). A degron-mimicking molecular glue drives CRBN homo-dimerization and degradation. Nature Communications. 16(1). 10157–10157.

Gaínza, Pablo, R.D. Bunker, Sharon A. Townson, & John C. Castle. (2025). Machine learning to predict de novo protein–protein interactions. Trends in biotechnology. 43(12). 3056–3070. 3 indexed citations

Buckley, Stephen, Arne Schneuing, Martin Pačesa, et al.. (2025). Targeting protein–ligand neosurfaces with a generalizable deep learning tool. Nature. 639(8054). 522–531. 19 indexed citations breakdown →

Attianese, Greta Maria Paola Giordano, Sailan Shui, Elisabetta Cribioli, et al.. (2024). Dual ON/OFF-switch chimeric antigen receptor controlled by two clinically approved drugs. Proceedings of the National Academy of Sciences. 121(44). e2405085121–e2405085121. 5 indexed citations

Bonati, Lucia, Sailan Shui, Leo Scheller, et al.. (2023). Rational Design of Chemically Controlled Antibodies and Protein Therapeutics. ACS Chemical Biology. 18(6). 1259–1265. 11 indexed citations

Masuho, Ikuo, Ryoji Kise, Pablo Gaínza, et al.. (2023). Rules and mechanisms governing G protein coupling selectivity of GPCRs. Cell Reports. 42(10). 113173–113173. 32 indexed citations

Oleinikovas, Vladimiras, Pablo Gaínza, Thomas Ryckmans, Bernhard Fasching, & Nicolas H. Thomä. (2023). From Thalidomide to Rational Molecular Glue Design for Targeted Protein Degradation. The Annual Review of Pharmacology and Toxicology. 64(1). 291–312. 89 indexed citations breakdown →

Wang, Siyu, Stephanie M. Reeve, Graham T. Holt, et al.. (2022). Chiral evasion and stereospecific antifolate resistance in Staphylococcus aureus. PLoS Computational Biology. 18(2). e1009855–e1009855. 6 indexed citations

Mason, Derek M., Simon Friedensohn, Cédric R. Weber, et al.. (2021). Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning. Nature Biomedical Engineering. 5(6). 600–612. 174 indexed citations

10.

Shui, Sailan, Pablo Gaínza, Leo Scheller, et al.. (2021). A rational blueprint for the design of chemically-controlled protein switches. Nature Communications. 12(1). 5754–5754. 21 indexed citations

11.

Gaínza, Pablo, Elise Gray-Gaillard, Elisabetta Cribioli, et al.. (2020). Author Correction: A computationally designed chimeric antigen receptor provides a small-molecule safety switch for T-cell therapy. Nature Biotechnology. 38(4). 503–503. 5 indexed citations

12.

Gaínza, Pablo, Elise Gray-Gaillard, Elisabetta Cribioli, et al.. (2020). A computationally designed chimeric antigen receptor provides a small-molecule safety switch for T-cell therapy. Nature Biotechnology. 38(4). 426–432. 103 indexed citations

13.

Sesterhenn, Fabian, Marie Galloux, Sabrina S. Vollers, et al.. (2019). Boosting subdominant neutralizing antibody responses with a computationally designed epitope-focused immunogen. PLoS Biology. 17(2). e3000164–e3000164. 24 indexed citations

14.

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 →

15.

Hallen, Mark A., Jeffrey W. Martin, Adegoke A. Ojewole, et al.. (2018). OSPREY 3.0: Open‐source protein redesign for you, with powerful new features. Journal of Computational Chemistry. 39(30). 2494–2507. 46 indexed citations

16.

Ojewole, Adegoke A., Pablo Gaínza, Stephanie M. Reeve, et al.. (2016). OSPREY Predicts Resistance Mutations Using Positive and Negative Computational Protein Design. Methods in molecular biology. 1529. 291–306. 10 indexed citations

17.

Gaínza, Pablo, Hunter Nisonoff, & Bruce R. Donald. (2016). Algorithms for protein design. Current Opinion in Structural Biology. 39. 16–26. 54 indexed citations

18.

Reeve, Stephanie M., Pablo Gaínza, Kathleen M. Frey, et al.. (2014). Protein design algorithms predict viable resistance to an experimental antifolate. Proceedings of the National Academy of Sciences. 112(3). 749–754. 39 indexed citations

19.

Gaínza, Pablo, Kyle E. Roberts, Ivelin S. Georgiev, et al.. (2013). osprey. Methods in enzymology on CD-ROM/Methods in enzymology. 523. 87–107. 86 indexed citations

20.

Gaínza, Pablo, Kyle E. Roberts, & Bruce R. Donald. (2012). Protein Design Using Continuous Rotamers. PLoS Computational Biology. 8(1). e1002335–e1002335. 76 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.

Explore authors with similar magnitude of impact