Ivan V. Korendovych

3.9k total citations · 1 hit paper
72 papers, 3.2k citations indexed

About

Ivan V. Korendovych is a scholar working on Molecular Biology, Organic Chemistry and Biomaterials. According to data from OpenAlex, Ivan V. Korendovych has authored 72 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 50 papers in Molecular Biology, 19 papers in Organic Chemistry and 19 papers in Biomaterials. Recurrent topics in Ivan V. Korendovych's work include Chemical Synthesis and Analysis (21 papers), Supramolecular Self-Assembly in Materials (18 papers) and Protein Structure and Dynamics (16 papers). Ivan V. Korendovych is often cited by papers focused on Chemical Synthesis and Analysis (21 papers), Supramolecular Self-Assembly in Materials (18 papers) and Protein Structure and Dynamics (16 papers). Ivan V. Korendovych collaborates with scholars based in United States, Ukraine and Japan. Ivan V. Korendovych's co-authors include William F. DeGrado, Olga V. Makhlynets, Yurii S. Moroz, Oleksii Zozulia, E.V. Rybak-Akimova, O.V. Moroz, Jan Stöhr, Pallavi M. Gosavi, Tyler A. Smith and Xiaozhen Hu and has published in prestigious journals such as Nature, Science and Proceedings of the National Academy of Sciences.

In The Last Decade

Ivan V. Korendovych

70 papers receiving 3.2k citations

Hit Papers

Short peptides self-assemble to produce catalytic amyloids 2014 2026 2018 2022 2014 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ivan V. Korendovych United States 30 2.0k 947 757 659 421 72 3.2k
Marcey L. Waters United States 38 2.8k 1.4× 488 0.5× 788 1.0× 1.7k 2.5× 309 0.7× 107 4.5k
Beate Koksch Germany 39 3.9k 1.9× 954 1.0× 664 0.9× 2.4k 3.7× 317 0.8× 178 6.2k
Angela Lombardi Italy 38 3.3k 1.6× 366 0.4× 1.3k 1.7× 1.0k 1.5× 1.0k 2.4× 161 5.0k
Javier Montenegro Spain 29 1.7k 0.8× 1.1k 1.1× 801 1.1× 1.2k 1.8× 181 0.4× 79 3.3k
Brice Kauffmann France 42 3.1k 1.5× 1.6k 1.7× 1.2k 1.6× 3.0k 4.6× 384 0.9× 180 5.1k
Annette Meister Germany 29 1.8k 0.9× 874 0.9× 593 0.8× 960 1.5× 96 0.2× 131 3.1k
Vincenzo Pavone Italy 27 1.8k 0.9× 201 0.2× 808 1.1× 679 1.0× 478 1.1× 74 2.8k
Mark Overhand Netherlands 34 2.8k 1.4× 358 0.4× 516 0.7× 1.8k 2.7× 134 0.3× 109 3.7k
Flavia Nastri Italy 29 1.9k 0.9× 174 0.2× 807 1.1× 637 1.0× 687 1.6× 89 3.0k
Jasmin Mecinović Netherlands 28 2.4k 1.2× 244 0.3× 463 0.6× 952 1.4× 293 0.7× 117 3.9k

Countries citing papers authored by Ivan V. Korendovych

Since Specialization
Citations

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

Fields of papers citing papers by Ivan V. Korendovych

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ivan V. Korendovych. 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 Ivan V. Korendovych. The network helps show where Ivan V. Korendovych may publish in the future.

Co-authorship network of co-authors of Ivan V. Korendovych

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

All Works

20 of 20 papers shown
1.
Kim, Inhye, et al.. (2025). Amyloid Peptide Nanofibrils Promote Efficient Neurotransmitter Oxidation and Serve as Scaffolds for Melanin Production. Angewandte Chemie International Edition. 64(44). e202510234–e202510234.
2.
Korendovych, Ivan V., et al.. (2024). Avoiding common pitfalls in designing kinetic protocols for catalytic amyloid studies. Methods in enzymology on CD-ROM/Methods in enzymology. 697. 1–13.
3.
Korendovych, Ivan V., et al.. (2023). Fishing for Catalysis: Experimental Approaches to Narrowing Search Space in Directed Evolution of Enzymes. SHILAP Revista de lepidopterología. 3(9). 2402–2412. 5 indexed citations
4.
Luo, Wei, et al.. (2022). De novo designed peptides form a highly catalytic ordered nanoarchitecture on a graphite surface. Nanoscale. 14(23). 8326–8331. 8 indexed citations
5.
Batra, Rohit, Troy D. Loeffler, Henry Chan, et al.. (2022). Machine learning overcomes human bias in the discovery of self-assembling peptides. Nature Chemistry. 14(12). 1427–1435. 100 indexed citations
6.
Margheritis, Eleonora, Inhye Kim, Jeremy R. H. Tame, et al.. (2022). NMR-guided directed evolution. Nature. 610(7931). 389–393. 36 indexed citations
7.
Zozulia, Oleksii, et al.. (2021). Self‐Assembling Catalytic Peptide Nanomaterials Capable of Highly Efficient Peroxidase Activity. Chemistry - A European Journal. 27(17). 5388–5392. 29 indexed citations
8.
Zozulia, Oleksii & Ivan V. Korendovych. (2020). Semi‐Rationally Designed Short Peptides Self‐Assemble and Bind Hemin to Promote Cyclopropanation. Angewandte Chemie International Edition. 59(21). 8108–8112. 50 indexed citations
9.
Zozulia, Oleksii & Ivan V. Korendovych. (2020). Semi‐Rationally Designed Short Peptides Self‐Assemble and Bind Hemin to Promote Cyclopropanation. Angewandte Chemie. 132(21). 8185–8189. 7 indexed citations
10.
Lengyel‐Zhand, Zsofia, et al.. (2020). Synergistic Interactions Are Prevalent in Catalytic Amyloids. ChemBioChem. 21(18). 2611–2614. 12 indexed citations
11.
Korendovych, Ivan V. & William F. DeGrado. (2020). De novoprotein design, a retrospective. Quarterly Reviews of Biophysics. 53. e3–e3. 163 indexed citations
12.
Lengyel‐Zhand, Zsofia, Yurii S. Moroz, Alexander N. Volkov, et al.. (2019). Kemp Eliminases of the AlleyCat Family Possess High Substrate Promiscuity. ChemCatChem. 11(5). 1425–1430. 3 indexed citations
13.
Basa, Prem N., et al.. (2019). Catalytic Nanoassemblies Formed by Short Peptides Promote Highly Enantioselective Transfer Hydrogenation. ACS Nano. 13(8). 9292–9297. 27 indexed citations
14.
Zozulia, Oleksii, et al.. (2019). Minimalist de Novo Design of Protein Catalysts. ACS Catalysis. 9(10). 9265–9275. 36 indexed citations
15.
Zozulia, Oleksii, et al.. (2018). Catalytic peptide assemblies. Chemical Society Reviews. 47(10). 3621–3639. 222 indexed citations
16.
Gosavi, Pallavi M., et al.. (2018). A Designed Enzyme Promotes Selective Post‐translational Acylation. ChemBioChem. 19(15). 1605–1608. 2 indexed citations
17.
Makhlynets, Olga V. & Ivan V. Korendovych. (2017). Finding a Silver Bullet in a Stack of Proteins. Biochemistry. 56(51). 6627–6628. 2 indexed citations
18.
Lengyel‐Zhand, Zsofia, et al.. (2017). Copper-Containing Catalytic Amyloids Promote Phosphoester Hydrolysis and Tandem Reactions. ACS Catalysis. 8(1). 59–62. 87 indexed citations
19.
Makhlynets, Olga V., Pallavi M. Gosavi, & Ivan V. Korendovych. (2016). Short Self‐Assembling Peptides Are Able to Bind to Copper and Activate Oxygen. Angewandte Chemie International Edition. 55(31). 9017–9020. 111 indexed citations
20.
Makhlynets, Olga V., Pallavi M. Gosavi, & Ivan V. Korendovych. (2016). Short Self‐Assembling Peptides Are Able to Bind to Copper and Activate Oxygen. Angewandte Chemie. 128(31). 9163–9166. 20 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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