World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
61
Citations
20522
World Ranking
3010
National Ranking
142

Igor V. Tetko publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Igor V. Tetko sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 250 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 560 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 192 publications — 43rd percentile

43% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Igor V. Tetko D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Igor V. Tetko sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 61 D-Index — 79th percentile

79% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Igor V. Tetko is affiliated with the Helmholtz Zentrum München in Germany. The scientist's research spans several fields primarily focused on computational and molecular sciences.

The main fields of study include:

  • Computer Science
  • Biochemistry, Genetics and Molecular Biology

Within these, the prominent subfields of study are:

  • Computational Theory and Mathematics
  • Molecular Biology
  • Materials Chemistry
  • Organic Chemistry
  • Artificial Intelligence

The main research topics Igor V. Tetko has worked on are:

  • Computational Drug Discovery Methods
  • Machine Learning in Materials Science
  • Metabolomics and Mass Spectrometry Studies
  • Protein Structure and Dynamics
  • Ionic liquids properties and applications
  • Machine Learning in Bioinformatics
  • Analytical Chemistry and Chromatography

Recent publications by Igor V. Tetko include:

  • "QSAR without borders," 2020, Chemical Society Reviews
  • "Transformer-CNN: Swiss knife for QSAR modeling and interpretation," 2020, Journal of Cheminformatics
  • "CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity," 2020, Environmental Health Perspectives
  • "CATMoS: Collaborative Acute Toxicity Modeling Suite," 2021, Environmental Health Perspectives
  • "Trade-off Predictivity and Explainability for Machine-Learning Powered Predictive Toxicology: An in-Depth Investigation with Tox21 Data Sets," 2021, Chemical Research in Toxicology

Frequent co-authors collaborating with Igor V. Tetko are:

  • Alexander Tropsha
  • Guillaume Godin
  • Alexandre Varnek
  • Denis Fourches
  • Eugene Muratov

Igor V. Tetko has a notable presence in several publication venues, with multiple articles published in these journals:

  • Chemical Research in Toxicology
  • Journal of Cheminformatics
  • arXiv (Cornell University)
  • Environmental Health Perspectives
  • Journal of Chemical Information and Modeling

In addition to peer-reviewed articles, the scientist has contributed to book publications, including a title titled "Abstracts of XXVII Symposium "Bioinformatics and Computer-Aided Drug Discovery"" published in 2021 by the Institute of Biomedical Chemistry, Moscow, Russia eBooks.

Best Publications

  • Virtual computational chemistry laboratory - design and description

    Igor V. Tetko;Johann Gasteiger;Roberto Todeschini;Andrea Mauri

  • Extended Functional Groups (EFG): An Efficient Set for Chemical Characterization and Structure-Activity Relationship Studies of Chemical Compounds

    Elena S. Salmina;Norbert Haider;Igor V. Tetko

  • The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes

    Andreas Ruepp;Alfred Zollner;Dieter Maier;Kaj Albermann

  • Neural network studies. 1. Comparison of overfitting and overtraining

    Igor V. Tetko;David J. Livingstone;Alexander I. Luik

  • QSAR without borders

    Eugene N. Muratov;Eugene N. Muratov;Jürgen Bajorath;Robert P. Sheridan;Igor V. Tetko

  • Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information

    Iurii Sushko;Sergii Novotarskyi;Robert Körner;Anil Kumar Pandey

  • Application of associative neural networks for prediction of lipophilicity in ALOGPS 2.1 program.

    Igor V. Tetko;Vsevolod Yu. Tanchuk

  • State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis.

    Igor V. Tetko;Pavel Karpov;Ruud Van Deursen;Guillaume Godin

  • Gene selection from microarray data for cancer classification-a machine learning approach

    Yu Wang;Igor V. Tetko;Mark A. Hall;Eibe Frank

  • Prediction of n-octanol/water partition coefficients from PHYSPROP database using artificial neural networks and E-state indices.

    Igor V. Tetko;Vsevolod Yu. Tanchuk;Alessandro E. P. Villa

  • Critical Assessment of QSAR Models of Environmental Toxicity against Tetrahymena pyriformis: Focusing on Applicability Domain and Overfitting by Variable Selection

    Igor V. Tetko;Iurii Sushko;Anil Kumar Pandey;Hao Zhu

  • Estimation of aqueous solubility of chemical compounds using E-state indices.

    Igor V. Tetko;Vsevolod Yu. Tanchuk;Tamara N. Kasheva;Alessandro E. P. Villa

  • CERAPP: Collaborative Estrogen Receptor Activity Prediction Project

    Kamel Mansouri;Ahmed Abdelaziz;Aleksandra Rybacka;Alessandra Roncaglioni

  • Combinatorial QSAR Modeling of Chemical Toxicants Tested against Tetrahymena pyriformis

    Hao Zhu;Alexander Tropsha;Denis Fourches;Alexandre Varnek

  • Can we estimate the accuracy of ADME-Tox predictions?

    Igor V. Tetko;Pierre Bruneau;Hans-Werner Mewes;Douglas C. Rohrer

  • Applicability domains for classification problems: Benchmarking of distance to models for Ames mutagenicity set.

    Iurii Sushko;Sergii Novotarskyi;Robert Körner;Anil Kumar Pandey

  • ToxAlerts: a Web server of structural alerts for toxic chemicals and compounds with potential adverse reactions.

    Iurii Sushko;Elena Salmina;Vladimir A. Potemkin;Gennadiy Poda

  • A renaissance of neural networks in drug discovery

    Igor I Baskin;David Winkler;Igor V Tetko

  • ISIDA - Platform for Virtual Screening Based on Fragment and Pharmacophoric Descriptors

    Alexandre Varnek;Denis Fourches;Dragos Horvath;Olga Klimchuk

  • Computing chemistry on the web.

    Igor V. Tetko

  • Transformer-CNN: Swiss knife for QSAR modeling and interpretation

    Pavel Karpov;Guillaume Godin;Igor V. Tetko

Frequent Co-Authors

Alessandro E. P. Villa
Alessandro E. P. Villa University of Lausanne
Alexander Tropsha
Alexander Tropsha University of North Carolina at Chapel Hill
Denis Fourches
Denis Fourches North Carolina State University
Hans-Werner Mewes
Hans-Werner Mewes Technical University of Munich
Eugene N. Muratov
Eugene N. Muratov University of North Carolina at Chapel Hill
Matthias Rupp
Matthias Rupp Luxembourg Institute of Science and Technology
Johann Gasteiger
Johann Gasteiger University of Erlangen-Nuremberg
Dragos Horvath
Dragos Horvath University of Strasbourg
Beat Schwaller
Beat Schwaller University of Fribourg
Hao Zhu
Hao Zhu Rutgers, The State University of New Jersey

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