Günter Klambauer

7.7k total citations · 3 hit papers
45 papers, 3.1k citations indexed

About

Günter Klambauer is a scholar working on Molecular Biology, Computational Theory and Mathematics and Materials Chemistry. According to data from OpenAlex, Günter Klambauer has authored 45 papers receiving a total of 3.1k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Molecular Biology, 21 papers in Computational Theory and Mathematics and 14 papers in Materials Chemistry. Recurrent topics in Günter Klambauer's work include Computational Drug Discovery Methods (21 papers), Machine Learning in Materials Science (14 papers) and Protein Structure and Dynamics (7 papers). Günter Klambauer is often cited by papers focused on Computational Drug Discovery Methods (21 papers), Machine Learning in Materials Science (14 papers) and Protein Structure and Dynamics (7 papers). Günter Klambauer collaborates with scholars based in Austria, United States and Belgium. Günter Klambauer's co-authors include Sepp Hochreiter, Andreas Mayr, Thomas Unterthiner, Djork-Arné Clevert, Jörg K. Wegner, Kristina Preuer, Andreas Bender, Richard P. Lewis, Krishna C. Bulusu and Hugo Ceulemans and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Günter Klambauer

44 papers receiving 3.0k citations

Hit 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.

DeepTox: Toxicity Prediction using Deep Learning

2016 638 citations Andreas Mayr, Günter Klambauer et al. profile →
DeepSynergy: predicting anti-cancer drug synergy with Deep Learning

2017 382 citations Kristina Preuer, Sepp Hochreiter et al. profile →
Large-scale comparison of machine learning methods for drug target prediction on ChEMBL

2018 352 citations Andreas Mayr, Günter Klambauer et al. profile →

Peers

Günter Klambauer

CTM

GENET

Djork-Arné Clevert Germany

Shanrong Zhao United States

Parantu K. Shah United States

Brian Kelley United States

Ola Spjuth Sweden

Andreas Mayr Austria

Pedro J. Ballester France

Rengül Çetin-Atalay Türkiye

Isidro Cortés‐Ciriano United Kingdom

Gerard J. P. van Westen Netherlands

Djork-Arné Clevert Germany

Günter Klambauer

1.1k ×0.7

CTM

1.5k ×1.0

765 ×1.0

197 ×0.6

299 ×1.0

GENET

Citations per year, relative to Günter Klambauer Günter Klambauer (= 1×) peers Djork-Arné Clevert

Countries citing papers authored by Günter Klambauer

Since Specialization

Citations

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

Fields of papers citing papers by Günter Klambauer

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Günter Klambauer

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

All Works

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20 of 20 papers shown

Brandstetter, J., et al.. (2025). VN-EGNN: E(3)- and SE(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification. Journal of Cheminformatics. 18(1). 11–11.

Hochreiter, Sepp, et al.. (2023). CLOOME: contrastive learning unlocks bioimaging databases for queries with chemical structures. Nature Communications. 14(1). 7339–7339. 24 indexed citations

Klotz, Daniel, Frederik Kratzert, Martin Gauch, et al.. (2022). Uncertainty estimation with deep learning for rainfall–runoff modeling. Hydrology and earth system sciences. 26(6). 1673–1693. 104 indexed citations

Akbar, Rahmad, Philippe A. Robert, Cédric R. Weber, et al.. (2022). In silico proof of principle of machine learning-based antibody design at unconstrained scale. mAbs. 14(1). 2031482–2031482. 50 indexed citations

Klotz, Daniel, Frederik Kratzert, Martin Gauch, et al.. (2021). Uncertainty Estimation with Deep Learning for Rainfall–Runoff Modelling. 13 indexed citations

Klotz, Daniel, Frederik Kratzert, Martin Gauch, et al.. (2021). Uncertainty estimation with LSTM based rainfall-runoff models. 1 indexed citations

Seidl, Philipp, Philipp Renz, Natalia Dyubankova, et al.. (2021). Modern Hopfield Networks for Few- and Zero-Shot Reaction Prediction.. arXiv (Cornell University). 1 indexed citations

Vall, Andreu, Yogesh Sabnis, Jiye Shi, et al.. (2021). The Promise of AI for DILI Prediction. Frontiers in Artificial Intelligence. 4. 638410–638410. 35 indexed citations

Mercado, Rocío, Edvard Lindelöf, Günter Klambauer, et al.. (2020). Graph networks for molecular design. Machine Learning Science and Technology. 2(2). 25023–25023. 102 indexed citations

10.

Sturm, Noé, Andreas Mayr, Vladimir Chupakhin, et al.. (2020). Industry-scale application and evaluation of deep learning for drug target prediction. Journal of Cheminformatics. 12(1). 26–26. 29 indexed citations

11.

Klotz, Daniel, Frederik Kratzert, Mathew Herrnegger, Sepp Hochreiter, & Günter Klambauer. (2019). Towards the quantification of uncertainty for deep learning based rainfall-runoff models. EGU General Assembly Conference Abstracts. 10708. 1 indexed citations

12.

Renz, Philipp, Dries Van Rompaey, Jörg K. Wegner, Sepp Hochreiter, & Günter Klambauer. (2019). On failure modes in molecule generation and optimization. Drug Discovery Today Technologies. 32-33. 55–63. 86 indexed citations

13.

Kratzert, Frederik, Daniel Klotz, Guy Shalev, et al.. (2019). Benchmarking a Catchment-Aware Long Short-Term MemoryNetwork (LSTM) for Large-Scale Hydrological Modeling. arXiv (Cornell University). 18 indexed citations

14.

Preuer, Kristina, Philipp Renz, Thomas Unterthiner, Sepp Hochreiter, & Günter Klambauer. (2018). Fréchet ChemblNet Distance: A metric for generative models for molecules.. arXiv (Cornell University). 2 indexed citations

15.

Hofmarcher, Markus, et al.. (2018). Human-level Protein Localization with Convolutional Neural Networks. International Conference on Learning Representations. 5 indexed citations

16.

Unterthiner, Thomas, Bernhard Nessler, Günter Klambauer, et al.. (2018). Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields. International Conference on Learning Representations. 6 indexed citations

17.

Simm, Jaak, Günter Klambauer, Ádám Arany, et al.. (2018). Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery. Cell chemical biology. 25(5). 611–618.e3. 140 indexed citations

18.

Schütz, Birgit, Günter Klambauer, Richard Moriggl, et al.. (2017). The unfolded protein response impacts melanoma progression by enhancing FGF expression and can be antagonized by a chemical chaperone. Scientific Reports. 7(1). 17498–17498. 24 indexed citations

19.

Wittwehr, Clemens, Hristo Aladjov, Gerald T. Ankley, et al.. (2016). How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology. Toxicological Sciences. 155(2). 326–336. 119 indexed citations

20.

Verbist, Bie, Günter Klambauer, Willem Talloen, et al.. (2015). Using transcriptomics to guide lead optimization in drug discovery projects: Lessons learned from the QSTAR project. Drug Discovery Today. 20(5). 505–513. 63 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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