Nir London

8.4k total citations
65 papers, 3.8k citations indexed

About

Nir London is a scholar working on Molecular Biology, Organic Chemistry and Computational Theory and Mathematics. According to data from OpenAlex, Nir London has authored 65 papers receiving a total of 3.8k indexed citations (citations by other indexed papers that have themselves been cited), including 49 papers in Molecular Biology, 15 papers in Organic Chemistry and 10 papers in Computational Theory and Mathematics. Recurrent topics in Nir London's work include Click Chemistry and Applications (13 papers), Protein Structure and Dynamics (12 papers) and Computational Drug Discovery Methods (10 papers). Nir London is often cited by papers focused on Click Chemistry and Applications (13 papers), Protein Structure and Dynamics (12 papers) and Computational Drug Discovery Methods (10 papers). Nir London collaborates with scholars based in Israel, United States and Canada. Nir London's co-authors include Ora Schueler‐Furman, Barak Raveh, Dana Movshovitz‐Attias, Ronen Gabizon, Daniel Zaidman, Eyal Cohen, Lior Zimmerman, Jaime Prilusky, Yinon Ben‐Neriah and Naama Kanarek and has published in prestigious journals such as Nature, Chemical Reviews and Proceedings of the National Academy of Sciences.

In The Last Decade

Nir London

65 papers receiving 3.7k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Nir London Israel	31	3.0k	666	602	534	480	65	3.8k
Matthew C. Franklin United States	24	2.6k 0.9×	779 1.2×	991 1.6×	636 1.2×	729 1.5×	41	4.5k
Traian Sulea Canada	29	1.8k 0.6×	430 0.6×	423 0.7×	329 0.6×	388 0.8×	113	2.9k
Lorenz M. Mayr Switzerland	28	2.3k 0.8×	309 0.5×	434 0.7×	247 0.5×	267 0.6×	51	3.1k
Andrew M. Petros United States	32	3.4k 1.1×	356 0.5×	659 1.1×	318 0.6×	196 0.4×	60	4.4k
Lidia Mosyak United States	29	2.1k 0.7×	446 0.7×	339 0.6×	226 0.4×	295 0.6×	45	3.4k
M.G. Rudolph Switzerland	34	2.3k 0.8×	342 0.5×	607 1.0×	328 0.6×	501 1.0×	89	4.5k
Djordje Müsil Germany	27	1.8k 0.6×	302 0.5×	564 0.9×	317 0.6×	147 0.3×	50	3.0k
Jeffrey R. Huth United States	26	2.2k 0.7×	637 1.0×	156 0.3×	548 1.0×	216 0.5×	40	3.3k
Sarel J. Fleishman Israel	40	4.6k 1.5×	346 0.5×	445 0.7×	195 0.4×	1.1k 2.3×	112	5.7k
Maxwell D. Cummings United States	26	1.6k 0.5×	498 0.7×	457 0.8×	577 1.1×	144 0.3×	52	2.9k

Countries citing papers authored by Nir London

Since Specialization

Citations

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

Fields of papers citing papers by Nir London

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nir London

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

1.

Oren, Roni, Bareket Dassa, A.N. Plotnikov, et al.. (2024). Dual targeting of histone deacetylases and MYC as potential treatment strategy for H3-K27M pediatric gliomas. eLife. 13. 3 indexed citations

2.

London, Nir, et al.. (2024). Deconvoluting low yield from weak potency in direct-to-biology workflows with machine learning. RSC Medicinal Chemistry. 15(3). 1015–1021. 3 indexed citations

3.

Oren, Roni, Bareket Dassa, A.N. Plotnikov, et al.. (2024). Dual targeting of histone deacetylases and MYC as potential treatment strategy for H3-K27M pediatric gliomas. eLife. 13. 3 indexed citations

4.

Vanhoutte, Roeland, Marta Barniol‐Xicota, Winston Chiu, et al.. (2023). Azapeptide activity-based probes for the SARS-CoV-2 main protease enable visualization of inhibition in infected cells. Chemical Science. 14(7). 1666–1672. 7 indexed citations

5.

Silver, Justin, et al.. (2022). Kidney Failure Alters Parathyroid Pin1 Phosphorylation and Parathyroid Hormone mRNA-Binding Proteins, Leading to Secondary Hyperparathyroidism. Journal of the American Society of Nephrology. 33(9). 1677–1693. 3 indexed citations

6.

Klompus, Shelley, Sigal Leviatan, Thomas Vogl, et al.. (2021). Cross-reactive antibodies against human coronaviruses and the animal coronavirome suggest diagnostics for future zoonotic spillovers. Science Immunology. 6(61). 30 indexed citations

7.

Barr, Haim, et al.. (2020). Structural basis for producing selective MAP2K7 inhibitors. Bioorganic & Medicinal Chemistry Letters. 30(22). 127546–127546. 4 indexed citations

8.

Correy, G.J., Daniel Zaidman, Alon Harmelin, et al.. (2019). Overcoming insecticide resistance through computational inhibitor design. Proceedings of the National Academy of Sciences. 116(42). 21012–21021. 34 indexed citations

9.

Reznik, Nava, Noga Kozer, Avital Eisenberg‐Lerner, et al.. (2019). Phenotypic Screen Identifies JAK2 as a Major Regulator of FAT10 Expression. ACS Chemical Biology. 14(12). 2538–2545. 2 indexed citations

10.

Shraga, Amit, Payam Khoshkenar, Nicolas Germain, et al.. (2018). Covalent Docking Identifies a Potent and Selective MKK7 Inhibitor. Cell chemical biology. 26(1). 98–108.e5. 47 indexed citations

11.

Ziarek, Joshua J., Andrew B. Kleist, Nir London, et al.. (2017). Structural basis for chemokine recognition by a G protein–coupled receptor and implications for receptor activation. Science Signaling. 10(471). 71 indexed citations

12.

London, Nir, Rand M. Miller, Shyam Krishnan, et al.. (2014). Covalent docking of large libraries for the discovery of chemical probes. Nature Chemical Biology. 10(12). 1066–1072. 208 indexed citations

13.

London, Nir, Rand M. Miller, John J. Irwin, et al.. (2014). Covalent Docking of Large Libraries for the Discovery of Chemical Probes. Biophysical Journal. 106(2). 264a–264a. 6 indexed citations

14.

Gao, Meng, Nir London, Kui Cheng, et al.. (2014). Rationally designed macrocyclic peptides as synergistic agonists of LPS-induced inflammatory response. Tetrahedron. 70(42). 7664–7668. 16 indexed citations

15.

London, Nir, Barak Raveh, & Ora Schueler‐Furman. (2013). Peptide docking and structure-based characterization of peptide binding: from knowledge to know-how. Current Opinion in Structural Biology. 23(6). 894–902. 86 indexed citations

16.

London, Nir, et al.. (2011). Identification of a Novel Class of Farnesylation Targets by Structure-Based Modeling of Binding Specificity. PLoS Computational Biology. 7(10). e1002170–e1002170. 53 indexed citations

17.

Movshovitz‐Attias, Dana, Nir London, & Ora Schueler‐Furman. (2010). On the use of structural templates for high‐resolution docking. Proteins Structure Function and Bioinformatics. 78(8). 1939–1949. 10 indexed citations

18.

London, Nir & Ora Schueler‐Furman. (2007). Assessing the energy landscape of CAPRI targets by FunHunt. Proteins Structure Function and Bioinformatics. 69(4). 809–815. 14 indexed citations

19.

Wang, Chu, Ora Schueler‐Furman, Ingemar André, et al.. (2007). RosettaDock in CAPRI rounds 6–12. Proteins Structure Function and Bioinformatics. 69(4). 758–763. 28 indexed citations

20.

London, Nir, et al.. (1985). Orchidopexy: theory and practice. BMJ. 291(6505). 1352.5–1352. 3 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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