World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
36
Citations
7679
World Ranking
11057
National Ranking
95

Günter Klambauer 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 Günter Klambauer 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: 88 publications — 5th percentile

5% of scientists in this discipline score the same or lower.

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

Günter Klambauer 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 Günter Klambauer 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: 36 D-Index — 23rd percentile

23% of scientists in this discipline score the same or lower.

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

Overview

Günter Klambauer is affiliated with Johannes Kepler University of Linz in Austria. Their research spans across multiple fields, prominently in Computer Science and Biochemistry, Genetics, and Molecular Biology. Their work includes notable contributions in subfields such as Molecular Biology, Computational Theory and Mathematics, Artificial Intelligence, Materials Chemistry, and Radiology, Nuclear Medicine and Imaging.

They have contributed extensively to the following main topics of research:

  • Computational Drug Discovery Methods
  • Machine Learning in Materials Science
  • Protein Structure and Dynamics
  • Vaccines and Immunoinformatics Approaches
  • Monoclonal and Polyclonal Antibodies Research
  • Cell Image Analysis Techniques
  • Machine Learning in Bioinformatics

Frequent publication venues for their work include:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Journal of Chemical Information and Modeling
  • Chemical Research in Toxicology
  • Hydrology and earth system sciences

Selected recent papers authored or coauthored by Günter Klambauer showcase a range of topics and areas:

  • Uncertainty estimation with deep learning for rainfall-runoff modeling, 2022, Hydrology and earth system sciences
  • Graph networks for molecular design, 2020, Machine Learning Science and Technology
  • In silico proof of principle of machine learning-based antibody design at unconstrained scale, 2022, mAbs
  • xLSTM: Extended Long Short-Term Memory, 2024, arXiv (Cornell University)
  • The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires, 2021, Nature Machine Intelligence

Collaborations form an important part of their research activity. Frequent coauthors include:

  • Sepp Hochreiter
  • Michael Widrich
  • J. Brandstetter
  • Geir Kjetil Sandve
  • Milena Pavlović

Best Publications

  • Self-Normalizing Neural Networks

    Günter Klambauer;Thomas Unterthiner;Andreas Mayr;Sepp Hochreiter

  • Self-Normalizing Neural Networks

    Günter Klambauer;Thomas Unterthiner;Andreas Mayr;Sepp Hochreiter

  • DeepTox: Toxicity Prediction using Deep Learning

    Andreas Mayr;Günter Klambauer;Thomas Unterthiner;Sepp Hochreiter

  • Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets

    Frederik Kratzert;Daniel Klotz;Guy Shalev;Günter Klambauer

  • DeepSynergy: predicting anti-cancer drug synergy with Deep Learning.

    Kristina Preuer;Richard P I Lewis;Sepp Hochreiter;Andreas Bender

  • Large-scale comparison of machine learning methods for drug target prediction on ChEMBL

    Andreas Mayr;Günter Klambauer;Thomas Unterthiner;Marvin Steijaert

  • cn.MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate

    Günter Klambauer;Karin Schwarzbauer;Andreas Mayr;Djork-Arné Clevert

  • GANs Trained by a Two Time-Scale Update Rule Converge to a Nash Equilibrium

    Martin Heusel;Hubert Ramsauer;Thomas Unterthiner;Bernhard Nessler

  • Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery

    Kristina Preuer;Philipp Renz;Thomas Unterthiner;Sepp Hochreiter

  • Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery

    Jaak Simm;Günter Klambauer;Adam Arany;Marvin Steijaert

  • How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology.

    Clemens Wittwehr;Hristo Aladjov;Gerald Ankley;Hugh J Byrne

  • Graph networks for molecular design

    Rocío Mercado;Tobias Rastemo;Tobias Rastemo;Edvard Lindelöf;Edvard Lindelöf;Günter Klambauer

  • xLSTM: Extended Long Short-Term Memory

    Unknown

  • On failure modes in molecule generation and optimization.

    Philipp Renz;Dries Van Rompaey;Jörg Kurt Wegner;Sepp Hochreiter

  • Accurate Prediction of Biological Assays with High-Throughput Microscopy Images and Convolutional Networks.

    Markus Hofmarcher;Elisabeth Rumetshofer;Djork-Arné Clevert;Sepp Hochreiter

  • Interpretable Deep Learning in Drug Discovery

    Kristina Preuer;Günter Klambauer;Friedrich Rippmann;Sepp Hochreiter

  • Hopfield Networks is All You Need

    Hubert Ramsauer;Bernhard Schäfl;Johannes Lehner;Philipp Seidl

  • Toxicity Prediction using Deep Learning

    Thomas Unterthiner;Andreas Mayr;Günter Klambauer;Sepp Hochreiter

  • Hopfield Networks is All You Need.

    Hubert Ramsauer;Bernhard Schäfl;Johannes Lehner;Philipp Seidl

  • NeuralHydrology -- Interpreting LSTMs in Hydrology

    Frederik Kratzert;Mathew Herrnegger;Daniel Klotz;Sepp Hochreiter

  • panelcn.MOPS: Copy-number detection in targeted NGS panel data for clinical diagnostics.

    Gundula Povysil;Antigoni Tzika;Julia Vogt;Verena Haunschmid

  • Machine Learning in Drug Discovery

    Günter Klambauer;Sepp Hochreiter;Matthias Rarey

  • The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires

    Milena Pavlovic;Lonneke Scheffer;Keshav Motwani;Chakravarthi Kanduri

  • DeepTox: Toxicity prediction using deep learning

    Günter Klambauer;Thomas Unterthiner;Andreas Mayr;Sepp Hochreiter

  • DeepRC: Immune repertoire classification with attention-based deep massive multiple instance learning

    Michael Widrich;Bernhard Schäfl;Milena Pavlović;Geir Kjetil Sandve

Frequent Co-Authors

Sepp Hochreiter
Sepp Hochreiter Johannes Kepler University of Linz
Ola Engkvist
Ola Engkvist AstraZeneca (United Kingdom)
Eivind Hovig
Eivind Hovig University of Oslo
Andreas Bender
Andreas Bender University of Cambridge
Ludvig M. Sollid
Ludvig M. Sollid Oslo University Hospital
Anne E. Carpenter
Anne E. Carpenter Broad Institute
Todd M. Brusko
Todd M. Brusko University of Florida
Yves Moreau
Yves Moreau KU Leuven
Ludwine Messiaen
Ludwine Messiaen University of Alabama at Birmingham
Hugh J. Byrne
Hugh J. Byrne Technological University Dublin

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