Igor V. Tetko

18.1k total citations · 6 hit papers
191 papers, 11.5k citations indexed

About

Igor V. Tetko is a scholar working on Computational Theory and Mathematics, Molecular Biology and Spectroscopy. According to data from OpenAlex, Igor V. Tetko has authored 191 papers receiving a total of 11.5k indexed citations (citations by other indexed papers that have themselves been cited), including 106 papers in Computational Theory and Mathematics, 55 papers in Molecular Biology and 44 papers in Spectroscopy. Recurrent topics in Igor V. Tetko's work include Computational Drug Discovery Methods (105 papers), Analytical Chemistry and Chromatography (42 papers) and Machine Learning in Materials Science (30 papers). Igor V. Tetko is often cited by papers focused on Computational Drug Discovery Methods (105 papers), Analytical Chemistry and Chromatography (42 papers) and Machine Learning in Materials Science (30 papers). Igor V. Tetko collaborates with scholars based in Germany, Ukraine and Switzerland. Igor V. Tetko's co-authors include Vsevolod Yu. Tanchuk, Alessandro E. P. Villa, David J. Livingstone, E.S. Salmina, N. Haider, Gennadiy Poda, Alexander I. Luik, Roberto Todeschini, Raimund Mannhold and Claude Ostermann and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Chemical Society Reviews and Nucleic Acids Research.

In The Last Decade

Igor V. Tetko

185 papers receiving 11.2k citations

Hit Papers

5 papers align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Virtual Computational Chemistry Laboratory – Design and Description

2005 1.3k citations Igor V. Tetko, David J. Livingstone et al. Journal of Computer-Aided Molecular Design profile →
Extended Functional Groups (EFG): An Efficient Set for Chemical Characterization and Structure-Activity Relationship Studies of Chemical Compounds

2015 890 citations Igor V. Tetko et al. profile →
QSAR without borders

2020 547 citations Eugene Muratov, Jürgen Bajorath et al. Chemical Society Reviews profile →
Neural network studies. 1. Comparison of overfitting and overtraining

1995 531 citations Igor V. Tetko, David J. Livingstone et al. profile →
Application of Associative Neural Networks for Prediction of Lipophilicity in ALOGPS 2.1 Program

2002 509 citations Igor V. Tetko et al. profile →
Calculation of Molecular Lipophilicity: State-of-the-Art and Comparison of LogP Methods on more than 96,000 Compounds

2008 507 citations Igor V. Tetko et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Igor V. Tetko Germany	50	5.0k	4.0k	2.2k	2.0k	1.6k	191	11.5k
Viviana Consonni Italy	35	6.4k 1.3×	3.1k 0.8×	1.5k 0.7×	2.4k 1.2×	1.5k 0.9×	97	10.9k
Roberto Todeschini Italy	47	8.0k 1.6×	3.9k 1.0×	1.9k 0.9×	3.4k 1.7×	2.2k 1.4×	185	14.1k
Robert P. Sheridan United States	54	5.7k 1.1×	9.7k 2.4×	2.7k 1.2×	1.0k 0.5×	1.2k 0.7×	126	16.3k
Anne Hersey United Kingdom	31	6.3k 1.3×	6.6k 1.7×	1.6k 0.7×	1.8k 0.9×	1.4k 0.9×	49	11.7k
Evan Bolton United States	28	5.1k 1.0×	7.3k 1.8×	1.6k 0.7×	1.1k 0.6×	892 0.5×	71	14.6k
Alexandre Varnek France	41	5.1k 1.0×	3.1k 0.8×	2.6k 1.2×	1.2k 0.6×	936 0.6×	221	7.9k
Paola Gramatica Italy	51	8.4k 1.7×	3.7k 0.9×	2.0k 0.9×	3.6k 1.8×	1.8k 1.1×	160	15.5k
Yu Chen China	69	5.3k 1.1×	10.0k 2.5×	1.2k 0.6×	1.5k 0.8×	1.1k 0.7×	455	17.6k
Johann Gasteiger Germany	49	7.9k 1.6×	7.0k 1.7×	2.8k 1.3×	3.6k 1.8×	3.1k 1.9×	251	17.0k
Dongsheng Cao China	57	5.7k 1.1×	6.6k 1.7×	2.0k 0.9×	1.4k 0.7×	870 0.5×	275	14.6k

Countries citing papers authored by Igor V. Tetko

Since Specialization

Citations

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

Fields of papers citing papers by Igor V. Tetko

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Igor V. Tetko

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Varbanov, Hristo P., Elisabetta Gabano, Mauro Ravera, et al.. (2025). Online OCHEM multi-task model for solubility and lipophilicity prediction of platinum complexes. Journal of Inorganic Biochemistry. 269. 112890–112890.

Krüger, Fabian, et al.. (2024). Using test-time augmentation to investigate explainable AI: inconsistencies between method, model and human intuition. Journal of Cheminformatics. 16(1). 39–39. 7 indexed citations

Tetko, Igor V., Ruud van Deursen, & Guillaume Godin. (2024). Be aware of overfitting by hyperparameter optimization!. Journal of Cheminformatics. 16(1). 139–139. 18 indexed citations

Šícho, Martin, et al.. (2024). The openOCHEM consensus model is the best-performing open-source predictive model in the First EUOS/SLAS joint compound solubility challenge. SLAS DISCOVERY. 29(2). 100144–100144. 11 indexed citations

Karpov, Pavel, et al.. (2022). What Features of Ligands Are Relevant to the Opening of Cryptic Pockets in Drug Targets?. Informatics. 9(1). 8–8. 2 indexed citations

Varnek, Alexandre, Igor V. Tetko, Alexander Tropsha, et al.. (2021). Abstracts of XXVII Symposium "Bioinformatics and Computer-Aided Drug Discovery". 2 indexed citations

Koch, Uwe, et al.. (2021). Highly Accurate Filters to Flag Frequent Hitters in AlphaScreen Assays by Suggesting their Mechanism. Molecular Informatics. 41(3). e2100151–e2100151. 2 indexed citations

Semenyuta, Ivan, Vasyl Kovalishyn, Sergiy Rogalsky, et al.. (2021). Structure-Activity Relationship Modeling and Experimental Validation of the Imidazolium and Pyridinium Based Ionic Liquids as Potential Antibacterials of MDR Acinetobacter baumannii and Staphylococcus aureus. International Journal of Molecular Sciences. 22(2). 563–563. 14 indexed citations

Muratov, Eugene, Jürgen Bajorath, Robert P. Sheridan, et al.. (2020). Correction: QSAR without borders. Chemical Society Reviews. 49(11). 3716–3716. 17 indexed citations

10.

Muratov, Eugene, Jürgen Bajorath, Robert P. Sheridan, et al.. (2020). QSAR without borders. Chemical Society Reviews. 49(11). 3525–3564. 547 indexed citations breakdown →

11.

Cortés‐Ciriano, Isidro, Wim Dehaen, Pavel Kříž, et al.. (2020). QSAR-derived affinity fingerprints (part 1): fingerprint construction and modeling performance for similarity searching, bioactivity classification and scaffold hopping. Journal of Cheminformatics. 12(1). 39–39. 27 indexed citations

12.

Karpov, Pavel, et al.. (2019). Focused Library Generator: case of Mdmx inhibitors. Journal of Computer-Aided Molecular Design. 34(7). 769–782. 8 indexed citations

13.

Tetko, Igor V., Věra Kůrková, Pavel Karpov, & Fabian J. Theis. (2019). Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part II. 8 indexed citations

14.

Dawidowski, Maciej, Kenji Schorpp, Leonidas Emmanouilidis, et al.. (2019). Structure–Activity Relationship in Pyrazolo[4,3-c]pyridines, First Inhibitors of PEX14–PEX5 Protein–Protein Interaction with Trypanocidal Activity. Journal of Medicinal Chemistry. 63(2). 847–879. 15 indexed citations

15.

Tetko, Igor V., Věra Kůrková, Pavel Karpov, & Fabian J. Theis. (2019). Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part I. 7 indexed citations

16.

Gimadiev, Timur, Timur Madzhidov, Igor V. Tetko, et al.. (2018). Bimolecular Nucleophilic Substitution Reactions: Predictive Models for Rate Constants and Molecular Reaction Pairs Analysis. Molecular Informatics. 38(4). e1800104–e1800104. 26 indexed citations

17.

Chen, Hongming, et al.. (2017). Matched Molecular Pair Analysis on Large Melting Point Datasets: A Big Data Perspective. ChemMedChem. 13(6). 599–606. 11 indexed citations

18.

Tetko, Igor V., Ola Engkvist, Uwe Koch, Jean‐Louis Reymond, & Hongming Chen. (2015). Illuminating flash point: comprehensive prediction models. RMIT Research Repository (RMIT University Library). 4 indexed citations

19.

Tetko, Igor V.. (2008). Internet in Drug Design and Discovery. Site cant be reached. 2(1). 18–21. 3 indexed citations

20.

Tetko, Igor V., et al.. (2000). Polynomial Neural Network for Linear and Non-linear Model Selection in Quantitative-Structure Activity Relationship Studies on the Internet. SAR and QSAR in environmental research. 11(3-4). 263–280. 23 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.

Explore authors with similar magnitude of impact