Dmitry Molodenskiy

1.6k total citations · 1 hit paper
17 papers, 954 citations indexed

About

Dmitry Molodenskiy is a scholar working on Molecular Biology, Materials Chemistry and Surfaces, Coatings and Films. According to data from OpenAlex, Dmitry Molodenskiy has authored 17 papers receiving a total of 954 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 10 papers in Materials Chemistry and 3 papers in Surfaces, Coatings and Films. Recurrent topics in Dmitry Molodenskiy's work include Protein Structure and Dynamics (8 papers), Enzyme Structure and Function (7 papers) and Protein purification and stability (2 papers). Dmitry Molodenskiy is often cited by papers focused on Protein Structure and Dynamics (8 papers), Enzyme Structure and Function (7 papers) and Protein purification and stability (2 papers). Dmitry Molodenskiy collaborates with scholars based in Germany, Russia and United States. Dmitry Molodenskiy's co-authors include Dmitri I. Svergun, Cy M. Jeffries, Alexey Kikhney, Clemente Borges, Andrey Gruzinov, Haydyn D. T. Mertens, Nelly R. Hajizadeh, Petr V. Konarev, Maxim V. Petoukhov and Alejandro Panjkovich and has published in prestigious journals such as Bioinformatics, Journal of Molecular Biology and Advanced Functional Materials.

In The Last Decade

Dmitry Molodenskiy

17 papers receiving 947 citations

Hit Papers

ATSAS 3.0: expanded funct... 2020 2026 2022 2024 2020 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
Dmitry Molodenskiy Germany 10 611 284 72 71 63 17 954
Andrey Gruzinov Russia 11 568 0.9× 269 0.9× 59 0.8× 53 0.7× 46 0.7× 26 983
Łukasz Wieteska Poland 16 832 1.4× 320 1.1× 60 0.8× 67 0.9× 64 1.0× 31 1.3k
Clemente Borges Spain 3 481 0.8× 221 0.8× 42 0.6× 55 0.8× 52 0.8× 5 751
John M. Franklin United States 4 786 1.3× 278 1.0× 56 0.8× 89 1.3× 102 1.6× 8 1.1k
Matías Machado Uruguay 17 981 1.6× 252 0.9× 80 1.1× 47 0.7× 68 1.1× 31 1.2k
Michael Kovermann Germany 20 814 1.3× 302 1.1× 112 1.6× 76 1.1× 89 1.4× 59 1.2k
Lars‐Göran Mårtensson Sweden 19 681 1.1× 226 0.8× 81 1.1× 119 1.7× 48 0.8× 34 998
Søren Skou United States 5 456 0.7× 229 0.8× 49 0.7× 49 0.7× 41 0.7× 7 684
Aleksandra E. Badaczewska-Dawid Poland 11 849 1.4× 388 1.4× 93 1.3× 30 0.4× 40 0.6× 21 1.1k
L. Urbanikova Slovakia 9 540 0.9× 158 0.6× 50 0.7× 84 1.2× 70 1.1× 16 835

Countries citing papers authored by Dmitry Molodenskiy

Since Specialization
Citations

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

Fields of papers citing papers by Dmitry Molodenskiy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dmitry Molodenskiy

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

All Works

17 of 17 papers shown
1.
Molodenskiy, Dmitry, Valentin Maurer, Grzegorz Chojnowski, et al.. (2025). AlphaPulldown2—a general pipeline for high-throughput structural modeling. Bioinformatics. 41(3). 1 indexed citations
2.
Vela, Stefano Da, Daniel Franke, Dmytro Soloviov, et al.. (2025). AF4-to-SAXS: expanded characterization of nanoparticles and proteins at the P12 BioSAXS beamline. Journal of Synchrotron Radiation. 32(4). 971–985. 1 indexed citations
3.
Molodenskiy, Dmitry, Dmitri I. Svergun, & Alexey Kikhney. (2022). Artificial neural networks for solution scattering data analysis. Structure. 30(6). 900–908.e2. 13 indexed citations
4.
Schroer, Martin A., Stefano Da Vela, Dmitry Molodenskiy, et al.. (2022). Rigid-to-Flexible Transition in a Molecular Brush in a Good Solvent at a Semidilute Concentration. Langmuir. 38(17). 5226–5236. 7 indexed citations
5.
Rivero-Rodríguez, J. F., Carlos A. Elena‐Real, Dmitry Molodenskiy, et al.. (2022). PP2A is activated by cytochrome c upon formation of a diffuse encounter complex with SET/TAF-Iβ. Computational and Structural Biotechnology Journal. 20. 3695–3707. 5 indexed citations
6.
Bucher, Michael, Stephan Niebling, Dmitry Molodenskiy, et al.. (2021). Autism-associated SHANK3 missense point mutations impact conformational fluctuations and protein turnover at synapses. eLife. 10. 19 indexed citations
7.
Morozov, Dmitry, Vladimir Mironov, Galina S. Zamay, et al.. (2021). The role of SAXS and molecular simulations in 3D structure elucidation of a DNA aptamer against lung cancer. Molecular Therapy — Nucleic Acids. 25. 316–327. 18 indexed citations
8.
Molodenskiy, Dmitry, Dmitri I. Svergun, & Haydyn D. T. Mertens. (2021). MPBuilder: A PyMOL Plugin for Building and Refinement of Solubilized Membrane Proteins Against Small Angle X-ray Scattering Data. Journal of Molecular Biology. 433(11). 166888–166888. 10 indexed citations
9.
Niebuur, Bart‐Jan, Dmitry Molodenskiy, Dorthe Posselt, et al.. (2021). Highly Tunable Nanostructures in a Doubly pH‐Responsive Pentablock Terpolymer in Solution and in Thin Films. Advanced Functional Materials. 31(32). 10 indexed citations
10.
Manalastas-Cantos, Karen, Petr V. Konarev, Nelly R. Hajizadeh, et al.. (2020). ATSAS 3.0: expanded functionality and new tools for small-angle scattering data analysis. Journal of Applied Crystallography. 54(1). 343–355. 581 indexed citations breakdown →
11.
Voronov, V. P., et al.. (2020). Interplay between various crystalline and hexatic-B phases in 75OBC liquid crystal: X-ray diffraction and calorimetry study. Liquid Crystals. 47(9). 1366–1378. 4 indexed citations
12.
Graewert, Melissa A., Stefano Da Vela, Tobias Gräwert, et al.. (2020). Adding Size Exclusion Chromatography (SEC) and Light Scattering (LS) Devices to Obtain High-Quality Small Angle X-Ray Scattering (SAXS) Data. Crystals. 10(11). 975–975. 43 indexed citations
13.
Molodenskiy, Dmitry, Haydyn D. T. Mertens, & Dmitri I. Svergun. (2020). An automated data processing and analysis pipeline for transmembrane proteins in detergent solutions. Scientific Reports. 10(1). 8081–8081. 14 indexed citations
14.
Kikhney, Alexey, Clemente Borges, Dmitry Molodenskiy, Cy M. Jeffries, & Dmitri I. Svergun. (2019). SASBDB: Towards an automatically curated and validated repository for biological scattering data. Protein Science. 29(1). 66–75. 152 indexed citations
15.
Molodenskiy, Dmitry, Beata Wielgus‐Kutrowska, Christian Johannessen, et al.. (2018). β2-Type Amyloidlike Fibrils of Poly-l-glutamic Acid Convert into Long, Highly Ordered Helices upon Dissolution in Dimethyl Sulfoxide. The Journal of Physical Chemistry B. 122(50). 11895–11905. 6 indexed citations
17.
Molodenskiy, Dmitry, et al.. (2016). Time variations of the stresses in the source region of the Tohoku earthquake of March 11, 2011 (M = 9) from tidal response data. Izvestiya Physics of the Solid Earth. 52(2). 210–217. 1 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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