Sergey Sosnin

947 total citations
22 papers, 491 citations indexed

About

Sergey Sosnin is a scholar working on Computational Theory and Mathematics, Materials Chemistry and Spectroscopy. According to data from OpenAlex, Sergey Sosnin has authored 22 papers receiving a total of 491 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Computational Theory and Mathematics, 11 papers in Materials Chemistry and 9 papers in Spectroscopy. Recurrent topics in Sergey Sosnin's work include Computational Drug Discovery Methods (17 papers), Machine Learning in Materials Science (10 papers) and Analytical Chemistry and Chromatography (8 papers). Sergey Sosnin is often cited by papers focused on Computational Drug Discovery Methods (17 papers), Machine Learning in Materials Science (10 papers) and Analytical Chemistry and Chromatography (8 papers). Sergey Sosnin collaborates with scholars based in Russia, Austria and United Kingdom. Sergey Sosnin's co-authors include Maxim V. Fedorov, Dmitry S. Karlov, Igor V. Tetko, Petr Popov, Pavel Karpov, Е. Н. Николаев, Yury Kostyukevich, Dmitrii Shadrin, Andrey Somov and Dmitry I. Osolodkin and has published in prestigious journals such as Analytical Chemistry, Scientific Reports and RSC Advances.

In The Last Decade

Sergey Sosnin

22 papers receiving 488 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sergey Sosnin Russia 12 289 225 175 84 39 22 491
Kohulan Rajan Germany 11 314 1.1× 347 1.5× 144 0.8× 62 0.7× 36 0.9× 29 681
Patrícia R. Oliveira Brazil 9 386 1.3× 302 1.3× 125 0.7× 40 0.5× 50 1.3× 24 591
Nikolas Fechner Germany 11 292 1.0× 218 1.0× 115 0.7× 50 0.6× 26 0.7× 21 404
Ignacio Ponzoni Argentina 16 301 1.0× 260 1.2× 131 0.7× 40 0.5× 103 2.6× 54 687
Alexios Koutsoukas United Kingdom 13 564 2.0× 487 2.2× 225 1.3× 50 0.6× 65 1.7× 26 906
Jeroen Kazius Netherlands 4 315 1.1× 202 0.9× 68 0.4× 72 0.9× 98 2.5× 4 551
Liu Shao United States 8 287 1.0× 154 0.7× 68 0.4× 47 0.6× 61 1.6× 22 481
Anil Kumar Pandey India 7 396 1.4× 217 1.0× 148 0.8× 72 0.9× 35 0.9× 19 591
Jonathan Alvarsson Sweden 13 495 1.7× 438 1.9× 161 0.9× 155 1.8× 57 1.5× 25 840
Yongna Yuan China 18 105 0.4× 223 1.0× 108 0.6× 56 0.7× 133 3.4× 48 737

Countries citing papers authored by Sergey Sosnin

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Sergey Sosnin

This figure shows the co-authorship network connecting the top 25 collaborators of Sergey Sosnin. A scholar is included among the top collaborators of Sergey Sosnin 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 Sergey Sosnin. Sergey Sosnin 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.
Sosnin, Sergey, et al.. (2025). BigSolDB 2.0, dataset of solubility values for organic compounds in different solvents at various temperatures. Scientific Data. 12(1). 1236–1236. 3 indexed citations
2.
Sosnin, Sergey. (2025). Chemical space visual navigation in the era of deep learning and Big Data. Drug Discovery Today. 30(7). 104392–104392. 4 indexed citations
3.
Sosnin, Sergey, et al.. (2024). The macrocycle inhibitor landscape of SLC‐transporter. Molecular Informatics. 43(5). e202300287–e202300287. 3 indexed citations
4.
Huang, Jiahui, Aidan MacNamara, Anders Mälarstig, et al.. (2024). ProteoMutaMetrics: machine learning approaches for solute carrier family 6 mutation pathogenicity prediction. RSC Advances. 14(19). 13083–13094. 1 indexed citations
5.
Sosnin, Sergey. (2024). MolCompass: multi-tool for the navigation in chemical space and visual validation of QSAR/QSPR models. Journal of Cheminformatics. 16(1). 98–98. 2 indexed citations
6.
Sosnin, Sergey, et al.. (2023). Improvement of multi-task learning by data enrichment: application for drug discovery. Journal of Computer-Aided Molecular Design. 37(4). 183–200. 7 indexed citations
7.
Fedorov, Maxim V., et al.. (2022). Image2SMILES: Transformer‐Based Molecular Optical Recognition Engine**. Chemistry - Methods. 2(1). 24 indexed citations
8.
Kostyukevich, Yury, et al.. (2022). PyFragMS─A Web Tool for the Investigation of the Collision-Induced Fragmentation Pathways. ACS Omega. 7(11). 9710–9719. 8 indexed citations
9.
Fedorov, Maxim V., et al.. (2021). Transformer-based artificial neural networks for the conversion between chemical notations. Scientific Reports. 11(1). 14798–14798. 29 indexed citations
10.
Shadrin, Dmitrii, et al.. (2021). Real-Time Detection of Hogweed: UAV Platform Empowered by Deep Learning. IEEE Transactions on Computers. 70(8). 1175–1188. 49 indexed citations
11.
Sosnin, Sergey, et al.. (2020). Recommender Systems in Antiviral Drug Discovery. ACS Omega. 5(25). 15039–15051. 15 indexed citations
12.
Sosnin, Sergey, et al.. (2020). Machine learning to predict retention time of small molecules in nano-HPLC. Analytical and Bioanalytical Chemistry. 412(28). 7767–7776. 33 indexed citations
13.
Karlov, Dmitry S., Sergey Sosnin, Maxim V. Fedorov, & Petr Popov. (2020). graphDelta: MPNN Scoring Function for the Affinity Prediction of Protein–Ligand Complexes. ACS Omega. 5(10). 5150–5159. 68 indexed citations
14.
Karlov, Dmitry S., Sergey Sosnin, Igor V. Tetko, & Maxim V. Fedorov. (2019). Chemical space exploration guided by deep neural networks. RSC Advances. 9(9). 5151–5157. 43 indexed citations
15.
Kostyukevich, Yury, Gleb Vladimirov, Alexander Zherebker, et al.. (2019). Hydrogen/Deuterium Exchange Aiding Compound Identification for LC-MS and MALDI Imaging Lipidomics. Analytical Chemistry. 91(21). 13465–13474. 20 indexed citations
16.
Osolodkin, Dmitry I., et al.. (2018). Influence of Descriptor Implementation on Compound Ranking Based on Multiparameter Assessment. Journal of Chemical Information and Modeling. 58(5). 1083–1093. 5 indexed citations
17.
Sosnin, Sergey, et al.. (2018). 3D matters! 3D-RISM and 3D convolutional neural network for accurate bioaccumulation prediction. Journal of Physics Condensed Matter. 30(32). 32LT03–32LT03. 14 indexed citations
18.
Sosnin, Sergey, et al.. (2018). A Survey of Multi‐task Learning Methods in Chemoinformatics. Molecular Informatics. 38(4). e1800108–e1800108. 63 indexed citations
19.
Sosnin, Sergey, Dmitry S. Karlov, Igor V. Tetko, & Maxim V. Fedorov. (2018). Comparative Study of Multitask Toxicity Modeling on a Broad Chemical Space. Journal of Chemical Information and Modeling. 59(3). 1062–1072. 74 indexed citations
20.
Radchenko, Eugene V., et al.. (2016). System for prediction of pharmacokinetic properties and toxicity of drug compounds. 4. 424. 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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