Daniel Kühn

3.4k total citations · 1 hit paper
28 papers, 1.9k citations indexed

About

Daniel Kühn is a scholar working on Molecular Biology, Computational Theory and Mathematics and Materials Chemistry. According to data from OpenAlex, Daniel Kühn has authored 28 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Molecular Biology, 13 papers in Computational Theory and Mathematics and 7 papers in Materials Chemistry. Recurrent topics in Daniel Kühn's work include Protein Structure and Dynamics (13 papers), Computational Drug Discovery Methods (13 papers) and Enzyme Structure and Function (4 papers). Daniel Kühn is often cited by papers focused on Protein Structure and Dynamics (13 papers), Computational Drug Discovery Methods (13 papers) and Enzyme Structure and Function (4 papers). Daniel Kühn collaborates with scholars based in Germany, Sweden and Jordan. Daniel Kühn's co-authors include G. Klebe, Friedrich Rippmann, Matthias Rarey, Andrea Volkamer, Stefan Schmitt, Thomas Grombacher, Nils Weskamp, Eyke Hüllermeier, Angela Casini and A. Heine and has published in prestigious journals such as Bioinformatics, Journal of Molecular Biology and Cancer Research.

In The Last Decade

Daniel Kühn

27 papers receiving 1.9k 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.

DoGSiteScorer: a web server for automatic binding site prediction, analysis and druggability assessment

2012 391 citations Andrea Volkamer, Daniel Kühn et al. Bioinformatics profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Daniel Kühn Germany	13	1.3k	763	319	280	250	28	1.9k
Oliver Korb United Kingdom	17	1.4k 1.0×	922 1.2×	461 1.4×	285 1.0×	235 0.9×	34	2.1k
Edward W. Lowe United States	11	976 0.7×	828 1.1×	314 1.0×	238 0.8×	175 0.7×	24	1.8k
Ian D. Wall United Kingdom	14	1.4k 1.0×	1.0k 1.3×	330 1.0×	280 1.0×	188 0.8×	26	1.9k
Sudipto Mukherjee United States	17	1.3k 1.0×	722 0.9×	250 0.8×	241 0.9×	148 0.6×	27	1.9k
Gregory Sliwoski United States	11	1.1k 0.8×	793 1.0×	296 0.9×	199 0.7×	182 0.7×	17	1.9k
Janez Konc Slovenia	24	1.2k 0.9×	691 0.9×	225 0.7×	278 1.0×	203 0.8×	74	1.8k
Erin S. D. Bolstad United States	11	1.3k 1.0×	942 1.2×	329 1.0×	303 1.1×	309 1.2×	15	2.3k
Sandeepkumar Kothiwale United States	8	952 0.7×	801 1.0×	298 0.9×	189 0.7×	165 0.7×	9	1.7k
Brian Y. Feng United States	11	1.2k 0.9×	599 0.8×	322 1.0×	157 0.6×	212 0.8×	18	1.9k
Andrea Volkamer Germany	20	1.4k 1.0×	936 1.2×	221 0.7×	293 1.0×	203 0.8×	55	2.1k

Countries citing papers authored by Daniel Kühn

Since Specialization

Citations

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

Fields of papers citing papers by Daniel Kühn

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Kühn

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Kühn. A scholar is included among the top collaborators of Daniel Kühn 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 Daniel Kühn. Daniel Kühn 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

1.

Friedrich, Lukas, et al.. (2024). Machine Learning Assisted Hit Prioritization for High Throughput Screening in Drug Discovery. ACS Central Science. 10(4). 823–832. 11 indexed citations

2.

Schindler, Christina, Daniel Kühn, & Ingo V. Hartung. (2023). The experiment is the limit. Nature Reviews Chemistry. 7(11). 752–753. 2 indexed citations

3.

Evers, Andreas, Shipra Malhotra, Maria Borisovska, et al.. (2023). SUMO: In Silico Sequence Assessment Using Multiple Optimization Parameters. Methods in molecular biology. 2681. 383–398. 8 indexed citations

4.

Grisoni, Francesca, et al.. (2023). Practical guidelines for the use of gradient boosting for molecular property prediction. Journal of Cheminformatics. 15(1). 73–73. 46 indexed citations

5.

Rusinko, Andrew, et al.. (2023). AIDDISON: Empowering Drug Discovery with AI/ML and CADD Tools in a Secure, Web-Based SaaS Platform. Journal of Chemical Information and Modeling. 64(1). 3–8. 11 indexed citations

6.

Friedrich, Lukas, et al.. (2022). Tuning gradient boosting for imbalanced bioassay modelling with custom loss functions. Journal of Cheminformatics. 14(1). 80–80. 11 indexed citations

7.

Buchstaller, Hans‐Peter, Dieter Dorsch, Daniel Kühn, et al.. (2019). Discovery and Optimization of 2-Arylquinazolin-4-ones into a Potent and Selective Tankyrase Inhibitor Modulating Wnt Pathway Activity. Journal of Medicinal Chemistry. 62(17). 7897–7909. 28 indexed citations

8.

Schindler, Christina, Friedrich Rippmann, & Daniel Kühn. (2017). Relative binding affinity prediction of farnesoid X receptor in the D3R Grand Challenge 2 using FEP+. Journal of Computer-Aided Molecular Design. 32(1). 265–272. 9 indexed citations

9.

Volkamer, Andrea, Daniel Kühn, Friedrich Rippmann, & Matthias Rarey. (2012). Predicting enzymatic function from global binding site descriptors. Proteins Structure Function and Bioinformatics. 81(3). 479–489. 11 indexed citations

10.

Volkamer, Andrea, Daniel Kühn, Friedrich Rippmann, & Matthias Rarey. (2012). DoGSiteScorer: a web server for automatic binding site prediction, analysis and druggability assessment. Bioinformatics. 28(15). 2074–2075. 391 indexed citations breakdown →

11.

Weskamp, Nils, Eyke Hüllermeier, Daniel Kühn, & G. Klebe. (2007). Multiple Graph Alignment for the Structural Analysis of Protein Active Sites. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 4(2). 310–320. 35 indexed citations

12.

Kühn, Daniel, Nils Weskamp, Eyke Hüllermeier, & G. Klebe. (2007). Functional Classification of Protein Kinase Binding Sites Using Cavbase. ChemMedChem. 2(10). 1432–1447. 61 indexed citations

13.

Hüllermeier, Eyke, Nils Weskamp, Gerhard Klebe, & Daniel Kühn. (2007). Graph Alignment: Fuzzy Pattern Mining for the Structural Analysis of Protein Active Sites. Proceedings of ... IEEE International Conference on Fuzzy Systems. 1–6. 1 indexed citations

14.

Weskamp, Nils, Eyke Hüllermeier, Daniel Kühn, & G. Klebe. (2007). Multiple Graph Alignment for the Structural Analysis of Protein Active Sites. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 4(2). 310–320. 14 indexed citations

15.

Al‐Gharabli, Samer, Syed Tasadaque Ali Shah, Steffen Weik, et al.. (2006). An Efficient Method for the Synthesis of Peptide Aldehyde Libraries Employed in the Discovery of Reversible SARS Coronavirus Main Protease (SARS‐CoV M^pro) Inhibitors. ChemBioChem. 7(7). 1048–1055. 48 indexed citations

16.

Kühn, Daniel, Nils Weskamp, Stefan Schmitt, Eyke Hüllermeier, & G. Klebe. (2006). From the Similarity Analysis of Protein Cavities to the Functional Classification of Protein Families Using Cavbase. Journal of Molecular Biology. 359(4). 1023–1044. 75 indexed citations

17.

Weskamp, Nils, Eyke Hüllermeier, Daniel Kühn, & G. Klebe. (2004). Graph alignments: A new concept to detect conserved regions in protein active sites. 140(3). 131–140. 4 indexed citations

18.

Weskamp, Nils, Daniel Kühn, Eyke Hüllermeier, & G. Klebe. (2004). Efficient similarity search in protein structure databases by k-clique hashing. Bioinformatics. 20(10). 1522–1526. 34 indexed citations

19.

Weskamp, Nils, Daniel Kühn, Eyke Hüllermeier, & G. Klebe. (2003). Efficient Similarity Search in Protein Structure Databases: Improving Cliqae-Detection through Clique Hashing.. 179–184. 2 indexed citations

20.

Schmitt, Stefan, Daniel Kühn, & G. Klebe. (2002). A New Method to Detect Related Function Among Proteins Independent of Sequence and Fold Homology. Journal of Molecular Biology. 323(2). 387–406. 335 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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