Inna Goreshnik

3.9k total citations · 3 hit papers
14 papers, 1.1k citations indexed

About

Inna Goreshnik is a scholar working on Molecular Biology, Radiology, Nuclear Medicine and Imaging and Infectious Diseases. According to data from OpenAlex, Inna Goreshnik has authored 14 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Molecular Biology, 3 papers in Radiology, Nuclear Medicine and Imaging and 2 papers in Infectious Diseases. Recurrent topics in Inna Goreshnik's work include RNA and protein synthesis mechanisms (5 papers), Protein Structure and Dynamics (3 papers) and Monoclonal and Polyclonal Antibodies Research (3 papers). Inna Goreshnik is often cited by papers focused on RNA and protein synthesis mechanisms (5 papers), Protein Structure and Dynamics (3 papers) and Monoclonal and Polyclonal Antibodies Research (3 papers). Inna Goreshnik collaborates with scholars based in United States, France and Belgium. Inna Goreshnik's co-authors include David Baker, Lauren Carter, Lance Stewart, Brian Coventry, L. M. Miller, Longxing Cao, Rita E. Chen, Michael Diamond, James Brett Case and David Veesler and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Journal of the American Chemical Society.

In The Last Decade

Inna Goreshnik

13 papers receiving 1.1k citations

Hit Papers

De novo design of picomolar SARS-CoV-2 miniprotein inhibi... 2017 2026 2020 2023 2020 2017 2023 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. De novo design of picomolar SARS-CoV-2 miniprotein inhibitors
    2020 451 citations Longxing Cao, Inna Goreshnik et al. Science profile →
  2. Global analysis of protein folding using massively parallel design, synthesis, and testing
    2017 305 citations Sugyan M. Dixit, Tamuka M. Chidyausiku et al. Science profile →
  3. Improving de novo protein binder design with deep learning
    2023 158 citations Nathaniel R. Bennett, Brian Coventry et al. Nature Communications profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Inna Goreshnik United States 10 827 301 177 134 130 14 1.1k
Brian Coventry United States 10 713 0.9× 278 0.9× 145 0.8× 105 0.8× 133 1.0× 16 1.0k
Eva‐Maria Strauch United States 12 1.3k 1.6× 334 1.1× 413 2.3× 235 1.8× 190 1.5× 21 1.7k
Rebecca F. Alford United States 6 990 1.2× 57 0.2× 152 0.9× 230 1.7× 124 1.0× 12 1.2k
Jeliazko R. Jeliazkov United States 13 1.3k 1.6× 86 0.3× 496 2.8× 239 1.8× 137 1.1× 21 1.6k
Cyrille Dreyfus United States 6 693 0.8× 46 0.2× 351 2.0× 125 0.9× 68 0.5× 9 970
Sebastian Kelm United Kingdom 17 888 1.1× 73 0.2× 486 2.7× 139 1.0× 93 0.7× 29 1.1k
Yih‐En Andrew Ban United States 9 476 0.6× 169 0.6× 208 1.2× 74 0.6× 39 0.3× 11 820
Kerstin Reiß Germany 15 450 0.5× 202 0.7× 43 0.2× 76 0.6× 32 0.2× 16 797
Jared Adolf‐Bryfogle United States 12 546 0.7× 110 0.4× 398 2.2× 43 0.3× 44 0.3× 18 714
Bastian Zimmermann Germany 22 994 1.2× 70 0.2× 116 0.7× 57 0.4× 30 0.2× 28 1.3k

Countries citing papers authored by Inna Goreshnik

Since Specialization
Citations

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

Fields of papers citing papers by Inna Goreshnik

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Inna Goreshnik

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

All Works

14 of 14 papers shown
1.
Huang, Buwei, Lieselotte S.M. Kreuk, Yensi Flores Bueso, et al.. (2025). De Novo Design of High‐Affinity Miniprotein Binders Targeting Francisella Tularensis Virulence Factor. Angewandte Chemie. 137(52).
2.
Glasscock, Cameron J., Ryan McHugh, Lindsey Doyle, et al.. (2025). Computational design of sequence-specific DNA-binding proteins. Nature Structural & Molecular Biology. 32(11). 2252–2261. 1 indexed citations
3.
Krishnakumar, Aditya, Robert J. Ragotte, Inna Goreshnik, et al.. (2024). Target-conditioned diffusion generates potent TNFR superfamily antagonists and agonists. Science. 386(6726). 1154–1161. 19 indexed citations
4.
Bennett, Nathaniel R., Brian Coventry, Inna Goreshnik, et al.. (2023). Improving de novo protein binder design with deep learning. Nature Communications. 14(1). 2625–2625. 158 indexed citations breakdown →
5.
Sahtoe, Danny D., Adrian Coscia, Nur Mustafaoğlu, et al.. (2021). Transferrin receptor targeting by de novo sheet extension. Proceedings of the National Academy of Sciences. 118(17). 26 indexed citations
6.
Bryan, Cassie M., Sugyan M. Dixit, Matthew J. Bick, et al.. (2021). Computational design of a synthetic PD-1 agonist. Proceedings of the National Academy of Sciences. 118(29). 46 indexed citations
7.
Case, James Brett, Rita E. Chen, Longxing Cao, et al.. (2021). Ultrapotent miniproteins targeting the SARS-CoV-2 receptor-binding domain protect against infection and disease. Cell Host & Microbe. 29(7). 1151–1161.e5. 40 indexed citations
8.
Basanta, Benjamin, Matthew J. Bick, Asim K. Bera, et al.. (2020). An enumerative algorithm for de novo design of proteins with diverse pocket structures. Proceedings of the National Academy of Sciences. 117(36). 22135–22145. 53 indexed citations
9.
Cao, Longxing, Inna Goreshnik, Brian Coventry, et al.. (2020). De novo design of picomolar SARS-CoV-2 miniprotein inhibitors. Science. 370(6515). 426–431. 451 indexed citations breakdown →
10.
Dou, Jiayi, Inna Goreshnik, Cassie M. Bryan, David Baker, & Eva‐Maria Strauch. (2019). Parallelized identification of on- and off-target protein interactions. Molecular Systems Design & Engineering. 5(1). 349–357. 1 indexed citations
11.
Dixit, Sugyan M., Tamuka M. Chidyausiku, Inna Goreshnik, et al.. (2017). Global analysis of protein folding using massively parallel design, synthesis, and testing. Science. 357(6347). 168–175. 305 indexed citations breakdown →
12.
Rose, John C., Po‐Ssu Huang, Nathan D. Camp, et al.. (2016). A computationally engineered RAS rheostat reveals RAS–ERK signaling dynamics. Nature Chemical Biology. 13(1). 119–126. 16 indexed citations
13.
Goreshnik, Inna, et al.. (2011). Biochemical and pharmacological profiling of the pro-survival protein Bcl-xL. Bioorganic & Medicinal Chemistry Letters. 21(17). 4951–4955. 4 indexed citations
14.
Goreshnik, Inna & Dustin J. Maly. (2009). A Small Molecule-Regulated Guanine Nucleotide Exchange Factor. Journal of the American Chemical Society. 132(3). 938–940. 22 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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