Valery Tkachenko

2.4k total citations
18 papers, 1.0k citations indexed

About

Valery Tkachenko is a scholar working on Computational Theory and Mathematics, Molecular Biology and Materials Chemistry. According to data from OpenAlex, Valery Tkachenko has authored 18 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Computational Theory and Mathematics, 7 papers in Molecular Biology and 5 papers in Materials Chemistry. Recurrent topics in Valery Tkachenko's work include Computational Drug Discovery Methods (11 papers), Machine Learning in Materials Science (5 papers) and Biomedical Text Mining and Ontologies (5 papers). Valery Tkachenko is often cited by papers focused on Computational Drug Discovery Methods (11 papers), Machine Learning in Materials Science (5 papers) and Biomedical Text Mining and Ontologies (5 papers). Valery Tkachenko collaborates with scholars based in United States, Russia and United Kingdom. Valery Tkachenko's co-authors include Antony Williams, Sean Ekins, Alexandru Korotcov, Daniel P. Russo, James L. Little, Nicole Kleinstreuer, Kamel Mansouri, David Allen, Neal F. Cariello and Chris Grulke and has published in prestigious journals such as Chemistry of Materials, Molecules and Drug Discovery Today.

In The Last Decade

Valery Tkachenko

18 papers receiving 969 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Valery Tkachenko United States	14	470	450	201	124	83	18	1.0k
Jonathan Alvarsson Sweden	13	438 0.9×	495 1.1×	161 0.8×	155 1.3×	57 0.7×	25	840
Joerg Wichard Germany	17	390 0.8×	481 1.1×	186 0.9×	47 0.4×	112 1.3×	38	1.1k
Hirotomo Moriwaki Japan	5	402 0.9×	607 1.3×	378 1.9×	124 1.0×	32 0.4×	8	1.1k
Marleen De Veij United Kingdom	9	577 1.2×	541 1.2×	229 1.1×	99 0.8×	36 0.4×	11	1.4k
Oliver Horlacher Switzerland	8	680 1.4×	583 1.3×	159 0.8×	239 1.9×	62 0.7×	10	1.1k
Qian‐Nan Hu China	20	775 1.6×	576 1.3×	154 0.8×	92 0.7×	34 0.4×	95	1.4k
Edgar Luttmann Germany	9	628 1.3×	605 1.3×	178 0.9×	177 1.4×	49 0.6×	10	1.0k
Liang Yu China	30	1.5k 3.2×	433 1.0×	153 0.8×	62 0.5×	117 1.4×	103	2.3k
Daniel P. Russo United States	20	509 1.1×	733 1.6×	378 1.9×	57 0.5×	98 1.2×	36	1.5k

Countries citing papers authored by Valery Tkachenko

Since Specialization

Citations

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

Fields of papers citing papers by Valery Tkachenko

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Valery Tkachenko

This figure shows the co-authorship network connecting the top 25 collaborators of Valery Tkachenko. A scholar is included among the top collaborators of Valery Tkachenko 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 Valery Tkachenko. Valery Tkachenko is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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18 of 18 papers shown

Sivasupramaniam, Sakuntala, et al.. (2025). Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods. Journal of Visualized Experiments. 5 indexed citations

Mansouri, Kamel, José Teófilo Moreira‐Filho, Charles N. Lowe, et al.. (2024). Free and open-source QSAR-ready workflow for automated standardization of chemical structures in support of QSAR modeling. Journal of Cheminformatics. 16(1). 19–19. 21 indexed citations

Lane, Thomas R., Fabio Urbina, Olga Riabova, et al.. (2021). Machine Learning Models for Mycobacterium tuberculosis In Vitro Activity: Prediction and Target Visualization. Molecular Pharmaceutics. 19(2). 674–689. 13 indexed citations

Mitrofanov, Artem, Petr I. Matveev, Alexandru Korotcov, et al.. (2021). Deep Learning Insights into Lanthanides Complexation Chemistry. Molecules. 26(11). 3237–3237. 3 indexed citations

Korolev, Vadim, Artem Mitrofanov, Artem A. Eliseev, & Valery Tkachenko. (2020). Machine-learning-assisted search for functional materials over extended chemical space. Materials Horizons. 7(10). 2710–2718. 17 indexed citations

Korolev, Vadim, et al.. (2020). Transferable and Extensible Machine Learning-Derived Atomic Charges for Modeling Hybrid Nanoporous Materials. Chemistry of Materials. 32(18). 7822–7831. 42 indexed citations

Mansouri, Kamel, Neal F. Cariello, Alexandru Korotcov, et al.. (2019). Open-source QSAR models for pKa prediction using multiple machine learning approaches. Journal of Cheminformatics. 11(1). 60–60. 119 indexed citations

Lane, Thomas R., Daniel P. Russo, Kimberley M. Zorn, et al.. (2018). Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery. Molecular Pharmaceutics. 15(10). 4346–4360. 82 indexed citations

Korotcov, Alexandru, Valery Tkachenko, Daniel P. Russo, & Sean Ekins. (2017). Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets. Molecular Pharmaceutics. 14(12). 4462–4475. 245 indexed citations

10.

Scalfani, Vincent F., Antony Williams, Valery Tkachenko, et al.. (2016). Programmatic conversion of crystal structures into 3D printable files using Jmol. Journal of Cheminformatics. 8(1). 66–66. 27 indexed citations

11.

Coles, Simon J., et al.. (2015). ChemTrove: Enabling a Generic ELN To Support Chemistry through the Use of Transferable Plug-ins and Online Data Sources. Journal of Chemical Information and Modeling. 55(3). 501–509. 10 indexed citations

12.

Karapetyan, K. V., Colin Batchelor, D R Sharpe, Valery Tkachenko, & Antony Williams. (2015). The Chemical Validation and Standardization Platform (CVSP): large-scale automated validation of chemical structure datasets. Journal of Cheminformatics. 7(1). 20 indexed citations

13.

Williams, Antony & Valery Tkachenko. (2014). The Royal Society of Chemistry and the delivery of chemistry data repositories for the community. Journal of Computer-Aided Molecular Design. 28(10). 1023–1030. 37 indexed citations

14.

Willighagen, Egon, Andra Waagmeester, Ola Spjuth, et al.. (2013). The ChEMBL database as linked open data. Journal of Cheminformatics. 5(1). 23–23. 78 indexed citations

15.

Williams, Antony, Sean Ekins, & Valery Tkachenko. (2012). Towards a gold standard: regarding quality in public domain chemistry databases and approaches to improving the situation. Drug Discovery Today. 17(13-14). 685–701. 90 indexed citations

16.

Little, James L., et al.. (2011). Identification of “Known Unknowns” Utilizing Accurate Mass Data and ChemSpider. Journal of the American Society for Mass Spectrometry. 23(1). 179–185. 134 indexed citations

17.

Hettne, Kristina, Antony Williams, Erik M. van Mulligen, et al.. (2010). Automatic vs. manual curation of a multi-source chemical dictionary: the impact on text mining. Journal of Cheminformatics. 2(1). 3–3. 37 indexed citations

18.

Williams, Antony, et al.. (2010). ChemSpider - building a foundation for the semantic web by hosting a crowd sourced databasing platform for chemistry. Journal of Cheminformatics. 2(S1). 21 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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