Sergey Sosnin
Impact in
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- Computational Drug Discovery Methods
- Spectroscopy top 10%
- Analytical Chemistry and Chromatography
- Mass Spectrometry Techniques and Applications
Papers in
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- Computational Drug Discovery Methods 17
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- Machine Learning in Materials Science 10
- Co-authors
- Maxim V. Fedorov (13 shared papers)Dmitry S. Karlov (3 shared papers)Igor V. Tetko (3 shared papers)Petr Popov (1 shared paper)L.V. Krasnov (3 shared papers)Pavel Karpov (2 shared papers)Yury Kostyukevich (3 shared papers)Е. Н. Николаев (3 shared papers)
- Journals
- ACS Omega (4 papers)RSC Advances (2 papers)Molecular Informatics (2 papers)Journal of Chemical Information and Modeling (2 papers)Journal of Physics Condensed Matter (1 paper)
- Partner nations
- RussiaAustriaUnited Kingdom
In The Last Decade
Sergey Sosnin
22 papers receiving 512 citations
Peers
Comparison fields: 5 of 99
- Computational Theory and Mathematics 290
- Spectroscopy 83
- Materials Chemistry 172
- Molecular Biology 220
- Biophysics 18
Countries citing papers authored by Sergey Sosnin
This map shows the geographic impact of Sergey Sosnin'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 Sergey Sosnin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sergey Sosnin more than expected).
Fields of papers citing papers by Sergey Sosnin
This network shows the impact of papers produced by Sergey Sosnin. 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 Sergey Sosnin. The network helps show where Sergey Sosnin may publish in the future.
Co-authors
The 25 scholars most cited alongside Sergey Sosnin, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 22 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 75 | |
| 2 | 2020 | 69 | |
| 3 | 2018 | 63 | |
| 4 | 2021 | 51 | |
| 5 | 2019 | 44 | |
| 6 | 2020 | 35 | |
| 7 | 2021 | 31 | |
| 8 | 2022 | 27 | |
| 9 | 2021 | 21 | |
| 10 | 2019 | 20 | |
| 11 | 2020 | 16 | |
| 12 | 2018 | 14 | |
| 13 | 2023 | 9 | |
| 14 | 2022 | 8 | |
| 15 | 2023 | 7 | |
| 16 | 2018 | 5 | |
| 17 | 2025 | 5 | |
| 18 | 2025 | 5 | |
| 19 | 2024 | 4 | |
| 20 | 2024 | 3 |
About Sergey Sosnin
Sergey Sosnin is a scholar working on Computational Theory and Mathematics, Materials Chemistry, Spectroscopy, Molecular Biology and Control and Systems Engineering, having authored 22 papers that have together received 515 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (17 papers), Machine Learning in Materials Science (10 papers), Analytical Chemistry and Chromatography (8 papers), Metabolomics and Mass Spectrometry Studies (4 papers), Chemistry and Chemical Engineering (2 papers), Protein Structure and Dynamics (2 papers), Mass Spectrometry Techniques and Applications (2 papers) and Isotope Analysis in Ecology (1 paper). The work is most often cited by research in Computational Theory and Mathematics (290 citations), Spectroscopy (83 citations), Materials Chemistry (172 citations), Molecular Biology (220 citations) and Biophysics (18 citations). Sergey Sosnin has collaborated with scholars based in Russia, Austria and United Kingdom. Frequent co-authors include Maxim V. Fedorov, Dmitry S. Karlov, Igor V. Tetko, Petr Popov, L.V. Krasnov, Pavel Karpov, Yury Kostyukevich, Е. Н. Николаев, Andrey Somov and Dmitrii Shadrin. Their work appears in journals such as ACS Omega, RSC Advances, Molecular Informatics, Journal of Chemical Information and Modeling and Journal of Physics Condensed Matter.
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.