Dmitri Kireev

3.9k total citations
76 papers, 2.0k citations indexed

About

Dmitri Kireev is a scholar working on Molecular Biology, Immunology and Computational Theory and Mathematics. According to data from OpenAlex, Dmitri Kireev has authored 76 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Molecular Biology, 25 papers in Immunology and 14 papers in Computational Theory and Mathematics. Recurrent topics in Dmitri Kireev's work include Phagocytosis and Immune Regulation (20 papers), Computational Drug Discovery Methods (14 papers) and Epigenetics and DNA Methylation (13 papers). Dmitri Kireev is often cited by papers focused on Phagocytosis and Immune Regulation (20 papers), Computational Drug Discovery Methods (14 papers) and Epigenetics and DNA Methylation (13 papers). Dmitri Kireev collaborates with scholars based in United States, Canada and Germany. Dmitri Kireev's co-authors include Stephen V. Frye, William P. Janzen, C.H. Arrowsmith, J. Martin Herold, Xiaodong Wang, Deborah DeRyckere, Douglas K. Graham, H. Shelton Earp, Michael A. Stashko and Masoud Vedadi and has published in prestigious journals such as Journal of the American Chemical Society, Nucleic Acids Research and Journal of Clinical Investigation.

In The Last Decade

Dmitri Kireev

68 papers receiving 2.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dmitri Kireev United States 25 1.3k 420 317 310 172 76 2.0k
A. Kuglstatter Switzerland 29 1.0k 0.8× 195 0.5× 284 0.9× 287 0.9× 268 1.6× 41 1.6k
Christian Grütter Germany 25 1.4k 1.0× 370 0.9× 301 0.9× 483 1.6× 400 2.3× 30 2.0k
John Sensintaffar United States 17 2.0k 1.5× 480 1.1× 294 0.9× 226 0.7× 424 2.5× 29 2.8k
Andrew J. Massey United Kingdom 24 1.5k 1.2× 205 0.5× 339 1.1× 227 0.7× 327 1.9× 43 2.0k
S. Betzi France 22 951 0.7× 110 0.3× 342 1.1× 197 0.6× 215 1.3× 38 1.5k
Simon Bergqvist United States 25 1.5k 1.1× 315 0.8× 158 0.5× 233 0.8× 599 3.5× 35 2.3k
Shiva Malek United States 23 2.2k 1.6× 605 1.4× 213 0.7× 208 0.7× 774 4.5× 35 3.1k
Richard Cummings United States 26 1.2k 0.9× 353 0.8× 121 0.4× 353 1.1× 417 2.4× 52 2.4k
Ursula Schulze‐Gahmen United States 25 1.8k 1.3× 225 0.5× 201 0.6× 209 0.7× 636 3.7× 38 2.5k
Bryan T. Mott United States 22 1.3k 0.9× 106 0.3× 284 0.9× 370 1.2× 198 1.2× 44 2.1k

Countries citing papers authored by Dmitri Kireev

Since Specialization
Citations

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

Fields of papers citing papers by Dmitri Kireev

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dmitri Kireev

This figure shows the co-authorship network connecting the top 25 collaborators of Dmitri Kireev. A scholar is included among the top collaborators of Dmitri Kireev 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 Dmitri Kireev. Dmitri Kireev 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.
Kong, Deyu, Jichen Zhao, Michael A. Stashko, et al.. (2025). Discovery of Novel TYRO3/MERTK Dual Inhibitors. Journal of Medicinal Chemistry. 68(8). 8455–8470.
2.
Williams, Michael R., et al.. (2023). Relationship between lysine methyltransferase levels and heterochromatin gene repression in living cells and in silico. PNAS Nexus. 2(4). pgad062–pgad062. 2 indexed citations
3.
Spangler, Cathy J., Anh Nguyen, C. B. Smith, et al.. (2023). Structural basis of paralog-specific KDM2A/B nucleosome recognition. Nature Chemical Biology. 19(5). 624–632. 13 indexed citations
4.
5.
Williams, Michael R., et al.. (2022). A simulation model of heterochromatin formation at submolecular detail. iScience. 25(7). 104590–104590. 3 indexed citations
6.
Lamb, Kelsey N., Huitao Fan, Jacob I. Stuckey, et al.. (2019). Discovery and Characterization of a Cellular Potent Positive Allosteric Modulator of the Polycomb Repressive Complex 1 Chromodomain, CBX7. Cell chemical biology. 26(10). 1365–1379.e22. 42 indexed citations
7.
Abramyan, Tigran M., Fengling Li, Masoud Vedadi, et al.. (2019). Discovery of selective activators of PRC2 mutant EED-I363M. Scientific Reports. 9(1). 6524–6524. 14 indexed citations
8.
An, Yi, Henning J. Jessen, Huanchen Wang, Stephen B. Shears, & Dmitri Kireev. (2019). Dynamics of Substrate Processing by PPIP5K2, a Versatile Catalytic Machine. Structure. 27(6). 1022–1028.e2. 9 indexed citations
9.
Stashko, Michael A., Huanchen Wang, Vikas Tyagi, et al.. (2018). Use of Protein Kinase–Focused Compound Libraries for the Discovery of New Inositol Phosphate Kinase Inhibitors. SLAS DISCOVERY. 23(9). 982–988. 18 indexed citations
10.
Minson, Katherine A., Catherine C. Smith, Deborah DeRyckere, et al.. (2016). The MERTK/FLT3 inhibitor MRX-2843 overcomes resistance-conferring FLT3 mutations in acute myeloid leukemia. JCI Insight. 1(3). e85630–e85630. 61 indexed citations
11.
Kireev, Dmitri. (2016). Structure-Based Virtual Screening of Commercially Available Compound Libraries. Methods in molecular biology. 1439. 65–76. 6 indexed citations
12.
Stashko, Michael A., et al.. (2015). Discovery of Mer kinase inhibitors by virtual screening using Structural Protein–Ligand Interaction Fingerprints. Bioorganic & Medicinal Chemistry. 23(5). 1096–1101. 8 indexed citations
13.
Zhong, Nan, Aiping Dong, Bradley M. Dickson, et al.. (2013). The structure–activity relationships of L3MBTL3 inhibitors: flexibility of the dimer interface. MedChemComm. 4(11). 1501–1501. 17 indexed citations
14.
Rothbart, Scott B., Bradley M. Dickson, Michelle S. Ong, et al.. (2013). Multivalent histone engagement by the linked tandem Tudor and PHD domains of UHRF1 is required for the epigenetic inheritance of DNA methylation. Genes & Development. 27(11). 1288–1298. 130 indexed citations
15.
Liu, Jing, Weihe Zhang, Michael A. Stashko, et al.. (2013). UNC1062, a new and potent Mer inhibitor. European Journal of Medicinal Chemistry. 65. 83–93. 55 indexed citations
16.
Hutti, Jessica E., Lewis C. Cantley, Xiaodong Wang, et al.. (2012). Development of a High-Throughput Assay for Identifying Inhibitors of TBK1 and IKKε. PLoS ONE. 7(7). e41494–e41494. 31 indexed citations
17.
Liu, Jing, Chao Yang, Catherine Simpson, et al.. (2012). Discovery of Small Molecule Mer Kinase Inhibitors for the Treatment of Pediatric Acute Lymphoblastic Leukemia. ACS Medicinal Chemistry Letters. 3(2). 129–134. 66 indexed citations
18.
Peterson, Eliza J. R., William P. Janzen, Dmitri Kireev, & Scott F. Singleton. (2011). High-Throughput Screening for RecA Inhibitors Using a Transcreener Adenosine 5′- O -Diphosphate Assay. Assay and Drug Development Technologies. 10(3). 260–268. 29 indexed citations
19.
Gao, Cen, J. Martin Herold, & Dmitri Kireev. (2011). Assessment of free energy predictors for ligand binding to a methyllysine histone code reader. Journal of Computational Chemistry. 33(6). 659–665. 8 indexed citations
20.
Bernard, Philippe, et al.. (1999). Automated docking of 82 N-benzylpiperidine derivatives to mouse acetylcholinesterase and comparative molecular field analysis with 'natural' alignment. Journal of Computer-Aided Molecular Design. 13(4). 355–371. 24 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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