V.Y. Grigorev

458 total citations
49 papers, 353 citations indexed

About

V.Y. Grigorev is a scholar working on Computational Theory and Mathematics, Molecular Biology and Organic Chemistry. According to data from OpenAlex, V.Y. Grigorev has authored 49 papers receiving a total of 353 indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Computational Theory and Mathematics, 17 papers in Molecular Biology and 11 papers in Organic Chemistry. Recurrent topics in V.Y. Grigorev's work include Computational Drug Discovery Methods (36 papers), Analytical Chemistry and Chromatography (8 papers) and Cholinesterase and Neurodegenerative Diseases (7 papers). V.Y. Grigorev is often cited by papers focused on Computational Drug Discovery Methods (36 papers), Analytical Chemistry and Chromatography (8 papers) and Cholinesterase and Neurodegenerative Diseases (7 papers). V.Y. Grigorev collaborates with scholars based in Russia, United Kingdom and Moldova. V.Y. Grigorev's co-authors include Oleg A. Raevsky, John C. Dearden, Dmitri Kireev, Andrew Worth, Pavel Polishchuk, Vadim V. Tarasov, Gjumrakch Aliev, С. О. Бачурин, Yuri B. Porozov and Nagendra Sastry Yarla and has published in prestigious journals such as Molecules, Current Medicinal Chemistry and Journal of Chemical Information and Modeling.

In The Last Decade

V.Y. Grigorev

47 papers receiving 347 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
V.Y. Grigorev Russia 9 200 87 79 75 65 49 353
Julian Ivanov United States 12 266 1.3× 103 1.2× 73 0.9× 107 1.4× 59 0.9× 22 477
Svava Ósk Jónsdóttir Denmark 16 174 0.9× 123 1.4× 168 2.1× 84 1.1× 116 1.8× 31 556
Gopinath Ghosh India 12 300 1.5× 98 1.1× 97 1.2× 117 1.6× 37 0.6× 14 338
Polina V. Oliferenko United States 11 140 0.7× 178 2.0× 93 1.2× 81 1.1× 78 1.2× 17 443
Christof H. Schwab Germany 9 279 1.4× 42 0.5× 184 2.3× 40 0.5× 64 1.0× 17 431
Sergii Novotarskyi Germany 7 269 1.3× 60 0.7× 122 1.5× 67 0.9× 114 1.8× 16 374
Antonio Chana Spain 16 110 0.6× 129 1.5× 117 1.5× 88 1.2× 50 0.8× 27 556
Tomasz Magdziarz Poland 11 291 1.5× 95 1.1× 206 2.6× 68 0.9× 59 0.9× 34 524
Omar Deeb Palestinian Territory 16 305 1.5× 175 2.0× 242 3.1× 131 1.7× 44 0.7× 54 746
Gilles Klopman United States 12 212 1.1× 74 0.9× 134 1.7× 132 1.8× 55 0.8× 19 533

Countries citing papers authored by V.Y. Grigorev

Since Specialization
Citations

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

Fields of papers citing papers by V.Y. Grigorev

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by V.Y. Grigorev. 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 V.Y. Grigorev. The network helps show where V.Y. Grigorev may publish in the future.

Co-authorship network of co-authors of V.Y. Grigorev

This figure shows the co-authorship network connecting the top 25 collaborators of V.Y. Grigorev. A scholar is included among the top collaborators of V.Y. Grigorev 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 V.Y. Grigorev. V.Y. Grigorev 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.
Grigorev, V.Y., et al.. (2024). HT_PREDICT: a machine learning-based computational open-source tool for screening HDAC6 inhibitors. SAR and QSAR in environmental research. 35(6). 505–530. 1 indexed citations
2.
Grigorev, V.Y., et al.. (2024). HDAC3_VS_assistant: cheminformatics-driven discovery of histone deacetylase 3 inhibitors. Molecular Diversity. 29(4). 3165–3187. 1 indexed citations
3.
Grigorev, V.Y., et al.. (2023). HDAC2 SCAN: An Expert System for Virtual Screening of Histone Deacetylase 2 Inhibitors. Russian Journal of General Chemistry. 93(S2). S426–S437.
4.
Grigorev, V.Y., et al.. (2023). HDAC6 detector: online application for evaluating compounds as potential histone deacetylase 6 inhibitors. SAR and QSAR in environmental research. 34(8). 619–637. 2 indexed citations
5.
Grigorev, V.Y., et al.. (2022). Structural fractal analysis of the active sites of acetylcholinesterase from various organisms. Journal of Molecular Graphics and Modelling. 116. 108265–108265. 2 indexed citations
6.
Grigorev, V.Y., et al.. (2022). QSAR Analysis of HDAC6 Inhibitors. Moscow University Chemistry Bulletin. 77(S1). S25–S35. 2 indexed citations
7.
Grigorev, V.Y., et al.. (2022). QSAR analysis and experimental evaluation of new quinazoline-containing hydroxamic acids as histone deacetylase 6 inhibitors. SAR and QSAR in environmental research. 33(7). 513–532. 7 indexed citations
8.
Grigorev, V.Y., et al.. (2021). SCALE AND LONG-TERM DYNAMICS OF OKA RIVER BASIN POLLUTION. 26(2). 40–53. 1 indexed citations
9.
Grigorev, V.Y., et al.. (2020). Effect of the structural factors of organic compounds on the acute toxicity toward Daphnia magna. SAR and QSAR in environmental research. 31(8). 615–641. 7 indexed citations
10.
Kazachenko, Vladimir P., et al.. (2020). Memory Effect in the Spatial Series Based on Diamond and Graphite Crystals. Molecules. 25(22). 5387–5387. 2 indexed citations
11.
Grigorev, V.Y., et al.. (2019). Outlier Detection in QSAR Modeling of the Biological Activity of Chemicals by Analyzing the Structure–Activity–Similarity Maps. Moscow University Chemistry Bulletin. 74(1). 1–9. 1 indexed citations
12.
Krisyuk, B. E., et al.. (2018). Molecular complexes and solvation interactions in the reaction of quinone imines with thiols. Russian Chemical Bulletin. 67(10). 1851–1856. 2 indexed citations
13.
Raevsky, Oleg A., et al.. (2018). QSAR Modeling Of Mammal Acute Toxicity By Oral Exposure. 1(3). e00066–e00066. 3 indexed citations
14.
Raevsky, Oleg A., V.Y. Grigorev, A. A. Ustyugov, et al.. (2018). Applications of Multi-Target Computer-Aided Methodologies in Molecular Design of CNS Drugs. Current Medicinal Chemistry. 25(39). 5293–5314. 12 indexed citations
15.
Raevsky, Oleg A., et al.. (2017). Six global and local QSPR models of aqueous solubility at pH = 7.4 based on structural similarity and physicochemical descriptors. SAR and QSAR in environmental research. 28(8). 661–676. 7 indexed citations
16.
Raevsky, Oleg A., et al.. (2015). In silico Prediction of Aqueous Solubility: a Comparative Study of Local and Global Predictive Models. Molecular Informatics. 34(6-7). 417–430. 26 indexed citations
17.
18.
Raevsky, Oleg A., et al.. (2011). Prediction of Acute Rodent Toxicity on the Basis of Chemical Structure and Physicochemical Similarity. Molecular Informatics. 30(2-3). 267–275. 13 indexed citations
19.
Raevsky, Oleg A., et al.. (2008). Classification and Quantification of the Toxicity of Chemicals to Guppy, Fathead Minnow and Rainbow Trout: Part 1. Nonpolar Narcosis Mode of Action. QSAR & Combinatorial Science. 27(11-12). 1274–1281. 34 indexed citations
20.
Grigorev, V.Y., et al.. (2004). The Importance of Molecular Parameters of Quaternary Ammonium Salts in Their Antigibberellin (Retardant) Activity. Russian Journal of Bioorganic Chemistry. 30(6). 592–598. 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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