Timur Madzhidov

2.0k citations
60 papers · 1.2k indexed · 1 hit paper · h-index 18
Topics
Computational Drug Discovery Methods (40 papers)Machine Learning in Materials Science (27 papers)Analytical Chemistry and Chromatography (9 papers)
Partner nations
RussiaFranceJapan

In The Last Decade

Timur Madzhidov

60 papers receiving 1.2k citations

Hit Papers

Estimation of the size of drug-like chemical space based ...20132026201720212013100200300

Peers

Timur Madzhidov
Comparison fields: 5 of 109
  • Computational Theory and Mathematics 818
  • Materials Chemistry 662
  • Molecular Biology 524
  • Organic Chemistry 168
  • Biomedical Engineering 147
Replace Ruud van Deursen with:
Ruud van Deursen Switzerland
Piotr Dittwald Poland
Sara Szymkuć Poland
Lars Ruddigkeit Switzerland
Karol Molga Poland
Jarosław M. Granda Poland
Volker Settels Germany
Burton A. Leland United States
Miriam Mathea Germany
Tomasz Klucznik South Korea
Timur Madzhidov relative to Ruud van Deursen Switzerland Ruud van Deursen's profile →
Citations per field
00.5×1.5×
Ruud van Deursen · 1×
Citations per year

Countries citing papers authored by Timur Madzhidov

Since Specialization
Citations

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

Fields of papers citing papers by Timur Madzhidov

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Timur Madzhidov

This figure shows the co-authorship network connecting the top 25 collaborators of Timur Madzhidov. A scholar is included among the top collaborators of Timur Madzhidov 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 Timur Madzhidov. Timur Madzhidov 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 1
2 9
3 4
4 1
5 3
6 4
7 10
8 21
9 10
10 7
11 14
12 49
13 6
14 5
15 28
16 26
17 7
18 19
19 8
20 8

About Timur Madzhidov

Timur Madzhidov is a scholar working on Computational Theory and Mathematics, Physical and Theoretical Chemistry and Toxicology, having authored 60 papers that have together received 1.2k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (40 papers), Machine Learning in Materials Science (27 papers) and Analytical Chemistry and Chromatography (9 papers). The work is most often cited by research in Computational Theory and Mathematics (818 citations), Materials Chemistry (662 citations) and Physical and Theoretical Chemistry (96 citations). Timur Madzhidov has collaborated with scholars based in Russia, France and Japan. Frequent co-authors include Alexandre Varnek, Pavel Polishchuk, Ramil Nugmanov, Timur Gimadiev, Igor I. Baskin, И. С. Антипин, Г. А. Чмутова, Gilles Marcou, Olga Klimchuk and Arkadii Lin. Their work appears in journals such as Scientific Reports, International Journal of Molecular Sciences and Journal of Controlled Release.

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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