Maxim Totrov

12.0k citations
117 papers · 9.0k indexed · 2 hit papers · h-index 44
Topics
Computational Drug Discovery Methods (45 papers)Protein Structure and Dynamics (36 papers)HIV Research and Treatment (18 papers)

In The Last Decade

Maxim Totrov

112 papers receiving 8.7k citations

Hit Papers

ICM—A new method for protein modeling and design: Applica...1994202620042015199419944008001.2k

Peers

Maxim Totrov
Comparison fields: 5 of 156
  • Molecular Biology 6.2k
  • Computational Theory and Mathematics 2.8k
  • Materials Chemistry 1.5k
  • Pharmacology 917
  • Organic Chemistry 916
Replace Irina Massova with:
Irina Massova United States
Alexandre M. J. J. Bonvin Netherlands
Stefano Forli United States
Robert Abel United States
David B. Ascher Australia
Holger Gohlke Germany
Mike Tyers Canada
Tyler Day United States
Leah L. Frye United States
Daniel T. Mainz United States
Maxim Totrov relative to Irina Massova United States Irina Massova's profile →
Citations per field
00.5×1.5×2.0×
Irina Massova · 1×
Citations per year

Countries citing papers authored by Maxim Totrov

Since Specialization
Citations

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

Fields of papers citing papers by Maxim Totrov

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Maxim Totrov

This figure shows the co-authorship network connecting the top 25 collaborators of Maxim Totrov. A scholar is included among the top collaborators of Maxim Totrov 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 Maxim Totrov. Maxim Totrov 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
#WorkIndexed citations
1 0
2 0
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4 4
5 1
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7 3
8 9
9 13
10 6
11 8
12 7
13 4
14 37
15 29
16 50
17 38
18 381
19 87
20 108

About Maxim Totrov

Maxim Totrov is a scholar working on Virology, Computational Theory and Mathematics and Molecular Medicine, having authored 117 papers that have together received 9.0k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (45 papers), Protein Structure and Dynamics (36 papers) and HIV Research and Treatment (18 papers). The work is most often cited by research in Computational Theory and Mathematics (2.8k citations), Virology (581 citations) and Molecular Medicine (551 citations). Maxim Totrov has collaborated with scholars based in United States, United Kingdom and Germany. Frequent co-authors include Ruben Abagyan, Dmitry Kuznetsov, Juan Fernández‐Recio, Matthieu Schapira, Jianghong An, Olga Lomovskaya, Timothy Cardozo, Marco A. C. Neves, Badry Bursulaya and Charles L. Brooks. Their work appears in journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Blood.

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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