Pablo Gaínza

2.5k citations
25 papers · 1.3k · 3 hit papers · h-index 14

Impact in

Papers in

Pablo Gaínza

24 papers receiving 1.3k citations

Hit Papers

Targeting protein–ligand neosurfaces with a generalizable deep learning tool 2025 · 19 citations
190+2+4Years since publication100200300400

Peers

Pablo Gaínza
Comparison fields: 5 of 107
  • Computational Theory and Mathematics 259
  • Molecular Biology 994
  • Radiology, Nuclear Medicine and Imaging 258
  • Biotechnology 56
  • Oncology 157
Replace Gordon Lemmon with:
Gordon Lemmon United States
Gaoqi Weng China
Minkyung Baek United States
Brian Jiménez‐García Spain
Oliver Koch Germany
Barak Raveh Israel
Sergey Lyskov United States
Ian Walsh Singapore
Duncan E. Scott United Kingdom
Jeliazko R. Jeliazkov United States
Pablo Gaínza relative to Gordon Lemmon United States Gordon Lemmon's profile →
Citations per field
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Citations per year

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-authors

The 25 scholars most cited alongside Pablo Gaínza, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Pablo Gaínza Line = papers co-authored together Pablo Gaínza links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 25 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning
Hit paper breakdown →
2019449
2 2021174
3 2020103
4
From Thalidomide to Rational Molecular Glue Design for Targeted Protein Degradation
Hit paper breakdown →
202389
5 201386
6 201276
7 201654
8 201846
9 201439
10 202332
11 201924
12 202121
13
Targeting protein–ligand neosurfaces with a generalizable deep learning tool
Hit paper breakdown →
202519
14 201516
15 201512
16 202311
17 201610
18 20226
19 20205
20 20245

About Pablo Gaínza

Pablo Gaínza is a scholar working on Molecular Biology, Radiology, Nuclear Medicine and Imaging, Oncology, Ecology and Computational Theory and Mathematics, having authored 25 papers that have together received 1.3k indexed citations. Recurring topics across this work include Protein Structure and Dynamics (10 papers), RNA and protein synthesis mechanisms (7 papers), Monoclonal and Polyclonal Antibodies Research (5 papers), CAR-T cell therapy research (4 papers), Machine Learning in Bioinformatics (3 papers), Protein Degradation and Inhibitors (3 papers), Computational Drug Discovery Methods (3 papers) and Bacterial Genetics and Biotechnology (3 papers). The work is most often cited by research in Computational Theory and Mathematics (259 citations), Molecular Biology (994 citations), Radiology, Nuclear Medicine and Imaging (258 citations), Biotechnology (56 citations) and Oncology (157 citations). Pablo Gaínza has collaborated with scholars based in Switzerland, United States and United Kingdom. Frequent co-authors include Bruno E. Correia, Bruce R. Donald, Michael M. Bronstein, Freyr Sverrisson, Davide Boscaini, Federico Monti, Emanuele Rodolà, Kyle E. Roberts, Hunter Nisonoff and Thomas Ryckmans. Their work appears in journals such as Nature Communications, Nature Biotechnology, PLoS Computational Biology, Proceedings of the National Academy of Sciences and Nature Methods.

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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