Dmitry Filimonov

8.8k total citations · 3 hit papers
175 papers, 6.3k citations indexed

About

Dmitry Filimonov is a scholar working on Computational Theory and Mathematics, Molecular Biology and Pharmacology. According to data from OpenAlex, Dmitry Filimonov has authored 175 papers receiving a total of 6.3k indexed citations (citations by other indexed papers that have themselves been cited), including 119 papers in Computational Theory and Mathematics, 93 papers in Molecular Biology and 43 papers in Pharmacology. Recurrent topics in Dmitry Filimonov's work include Computational Drug Discovery Methods (119 papers), Pharmacogenetics and Drug Metabolism (37 papers) and Metabolomics and Mass Spectrometry Studies (35 papers). Dmitry Filimonov is often cited by papers focused on Computational Drug Discovery Methods (119 papers), Pharmacogenetics and Drug Metabolism (37 papers) and Metabolomics and Mass Spectrometry Studies (35 papers). Dmitry Filimonov collaborates with scholars based in Russia, United States and United Kingdom. Dmitry Filimonov's co-authors include Vladimir Poroikov, Alexey A. Lagunin, Anastasia V. Rudik, Tatyana A. Gloriozova, Pavel V. Pogodin, Alexey Zakharov, Дмитрий Сергеевич Дружиловский, Yu. V. Borodina, Alexander V. Dmitriev and Sergey M. Ivanov and has published in prestigious journals such as Chemical Society Reviews, SHILAP Revista de lepidopterología and Bioinformatics.

In The Last Decade

Dmitry Filimonov

162 papers receiving 6.1k citations

Hit Papers

Prediction of the Biological Activity S... 2000 2026 2008 2017 2014 2000 2020 250 500 750

Peers

Dmitry Filimonov
Dmitry Filimonov
Citations per year, relative to Dmitry Filimonov Dmitry Filimonov (= 1×) peers Alexey A. Lagunin

Countries citing papers authored by Dmitry Filimonov

Since Specialization
Citations

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

Fields of papers citing papers by Dmitry Filimonov

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dmitry Filimonov

This figure shows the co-authorship network connecting the top 25 collaborators of Dmitry Filimonov. A scholar is included among the top collaborators of Dmitry Filimonov 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 Dmitry Filimonov. Dmitry Filimonov 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
2.
Pogodin, Pavel V., Elena G. Salina, Victor V. Semenov, et al.. (2024). Ligand-based virtual screening and biological evaluation of inhibitors of Mycobacterium tuberculosis H37Rv. SAR and QSAR in environmental research. 35(1). 53–69. 3 indexed citations
3.
Ivanov, Sergey M., Anastasia V. Rudik, Alexey A. Lagunin, Dmitry Filimonov, & Vladimir Poroikov. (2024). DIGEP‐Pred 2.0: A web application for predicting drug‐induced cell signaling and gene expression changes. Molecular Informatics. 43(12). e202400032–e202400032. 1 indexed citations
4.
Filimonov, Dmitry, et al.. (2024). Quantitative Prediction of Human Immunodeficiency Virus Drug Resistance. Viruses. 16(7). 1132–1132. 3 indexed citations
5.
Druzhilovskiy, D.S., et al.. (2024). WWAD: the most comprehensive small molecule World Wide Approved Drug database of therapeutics. Frontiers in Pharmacology. 15. 1473279–1473279. 2 indexed citations
6.
Poroikov, Vladimir, Alexander V. Dmitriev, D.S. Druzhilovskiy, et al.. (2023). In Silico Estimation of the Safety of Pharmacologically Active Substances Using Machine Learning Methods: A Review. SHILAP Revista de lepidopterología. 11(4). 372–389.
7.
Tarasova, Olga, et al.. (2023). Identification of Molecular Mechanisms Involved in Viral Infection Progression Based on Text Mining: Case Study for HIV Infection. International Journal of Molecular Sciences. 24(2). 1465–1465. 4 indexed citations
8.
Rudik, Anastasia V., Alexander V. Dmitriev, Alexey A. Lagunin, Dmitry Filimonov, & Vladimir Poroikov. (2023). MetaTox 2.0: Estimating the Biological Activity Spectra of Drug-like Compounds Taking into Account Probable Biotransformations. ACS Omega. 8(48). 45774–45778. 4 indexed citations
9.
Filimonov, Dmitry, et al.. (2023). Prediction of pathogenic single amino acid substitutions using molecular fragment descriptors. Bioinformatics. 39(8). 4 indexed citations
10.
Бочарова, О. А., В. Е. Шевченко, V. G. Kucheryanu, et al.. (2022). Computer‐aided Evaluation of Polyvalent Medications’ Pharmacological Potential. Multiphytoadaptogen as a Case Study. Molecular Informatics. 42(1). e2200176–e2200176. 3 indexed citations
11.
Tarasova, Olga, et al.. (2022). Chemical named entity recognition in the texts of scientific publications using the naïve Bayes classifier approach. Journal of Cheminformatics. 14(1). 55–55. 8 indexed citations
12.
Muratov, Eugene, Jürgen Bajorath, Robert P. Sheridan, et al.. (2020). Correction: QSAR without borders. Chemical Society Reviews. 49(11). 3716–3716. 17 indexed citations
13.
Muratov, Eugene, Jürgen Bajorath, Robert P. Sheridan, et al.. (2020). QSAR without borders. Chemical Society Reviews. 49(11). 3525–3564. 547 indexed citations breakdown →
14.
Pogodin, Pavel V., Alexey A. Lagunin, Anastasia V. Rudik, et al.. (2018). How to Achieve Better Results Using PASS-Based Virtual Screening: Case Study for Kinase Inhibitors. Frontiers in Chemistry. 6. 133–133. 31 indexed citations
15.
Lagunin, Alexey A., Dmitry Druzhilovsky, Anastasia V. Rudik, et al.. (2016). Capacities of computer evaluation of hidden potential of phytochemicals of medicinal plants of the traditional Indian Ayurvedic medicine. Biochemistry (Moscow) Supplement Series B Biomedical Chemistry. 10(1). 43–54. 4 indexed citations
16.
Pogodin, Pavel V., Alexey A. Lagunin, Dmitry Filimonov, & Vladimir Poroikov. (2015). PASS Targets: Ligand-based multi-target computational system based on a public data and naïve Bayes approach. SAR and QSAR in environmental research. 26(10). 783–793. 57 indexed citations
17.
Ivanov, Sergey M., Alexey A. Lagunin, Pavel V. Pogodin, Dmitry Filimonov, & Vladimir Poroikov. (2015). Identification of Drug Targets Related to the Induction of Ventricular Tachyarrhythmia Through a Systems Chemical Biology Approach. Toxicological Sciences. 145(2). 321–336. 10 indexed citations
18.
Filimonov, Dmitry, Alexey A. Lagunin, Tatyana A. Gloriozova, et al.. (2014). PREDICTION OF BIOLOGICAL ACTIVITY SPECTRA OF ORGANIC COMPOUNDS USING WEB-RESOURCE PASS ONLINE. Chemistry of Heterocyclic Compounds. 483–499. 1 indexed citations
19.
Eleftheriou, Phaedra, Athina Geronikaki, Dimitra Hadjipavlou‐Litina, et al.. (2011). Fragment-based design, docking, synthesis, biological evaluation and structure–activity relationships of 2-benzo/benzisothiazolimino-5-aryliden-4-thiazolidinones as cycloxygenase/lipoxygenase inhibitors. European Journal of Medicinal Chemistry. 47(1). 111–124. 72 indexed citations
20.
Filimonov, Dmitry, et al.. (2010). Functional classification of proteins based on projection of amino acid sequences: application for prediction of protein kinase substrates. BMC Bioinformatics. 11(1). 313–313. 14 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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