World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

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

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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