Oliver Koch

2.7k total citations · 1 hit paper
89 papers, 1.9k citations indexed

About

Oliver Koch is a scholar working on Molecular Biology, Computational Theory and Mathematics and Organic Chemistry. According to data from OpenAlex, Oliver Koch has authored 89 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 54 papers in Molecular Biology, 22 papers in Computational Theory and Mathematics and 16 papers in Organic Chemistry. Recurrent topics in Oliver Koch's work include Computational Drug Discovery Methods (22 papers), Protein Structure and Dynamics (15 papers) and Enzyme Structure and Function (8 papers). Oliver Koch is often cited by papers focused on Computational Drug Discovery Methods (22 papers), Protein Structure and Dynamics (15 papers) and Enzyme Structure and Function (8 papers). Oliver Koch collaborates with scholars based in Germany, United Kingdom and United States. Oliver Koch's co-authors include Tom N. Grossmann, Adrian Glas, Marta Pelay‐Gimeno, Christiane Ehrt, G. Klebe, Lina Humbeck, Mauro S. Nogueira, Gunter Lipowsky, Carolin Welter and Petra Mutzel and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Angewandte Chemie International Edition.

In The Last Decade

Oliver Koch

84 papers receiving 1.9k citations

Hit Papers

Structure‐Based Design of Inhibitors of Protein–Protein I... 2015 2026 2018 2022 2015 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Oliver Koch Germany 21 1.4k 576 386 205 186 89 1.9k
Iris Antes Germany 25 1.1k 0.8× 327 0.6× 201 0.5× 122 0.6× 110 0.6× 67 1.8k
Renate Griffith Australia 27 1.0k 0.8× 641 1.1× 240 0.6× 129 0.6× 64 0.3× 90 2.0k
Martin J. Drysdale United Kingdom 30 2.1k 1.5× 704 1.2× 664 1.7× 171 0.8× 224 1.2× 61 2.9k
Renée L. DesJarlais United States 28 1.5k 1.1× 727 1.3× 768 2.0× 264 1.3× 230 1.2× 61 2.5k
Francesca Milletti United States 18 1.4k 1.0× 283 0.5× 415 1.1× 511 2.5× 179 1.0× 32 2.2k
Edward W. Lowe United States 11 976 0.7× 314 0.5× 828 2.1× 142 0.7× 238 1.3× 24 1.8k
Jeffrey R. Huth United States 26 2.2k 1.6× 548 1.0× 637 1.7× 156 0.8× 254 1.4× 40 3.3k
Konrad Bleicher Switzerland 18 903 0.7× 388 0.7× 405 1.0× 137 0.7× 120 0.6× 38 1.5k
Shelli R. McAlpine Australia 28 1.6k 1.2× 586 1.0× 255 0.7× 187 0.9× 202 1.1× 87 2.2k
Gregory Sliwoski United States 11 1.1k 0.8× 296 0.5× 793 2.1× 185 0.9× 199 1.1× 17 1.9k

Countries citing papers authored by Oliver Koch

Since Specialization
Citations

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

Fields of papers citing papers by Oliver Koch

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Oliver Koch

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

All Works

20 of 20 papers shown
1.
Wagner, Stefan, Angelika Griep, Róisín M. McManus, et al.. (2025). Naphtho[1,2- b ][1,4]diazepinedione-Based P2X4 Receptor Antagonists from Structure–Activity Relationship Studies toward PET Tracer Development. Journal of Medicinal Chemistry. 68(7). 6965–7002. 4 indexed citations
2.
Koch, Oliver, et al.. (2024). Piperazine‐based P2X4 receptor antagonists. Archiv der Pharmazie. 358(1). e2400860–e2400860.
3.
Koch, Oliver, et al.. (2023). Introduction to artificial intelligence and deep learning using interactive electronic programming notebooks. Archiv der Pharmazie. 356(7). 3 indexed citations
4.
Koch, Oliver, Samuel Pironon, Tim Pagella, et al.. (2021). Modelling potential range expansion of an underutilised food security crop in Sub-Saharan Africa. Environmental Research Letters. 17(1). 14022–14022. 20 indexed citations
5.
Koch, Oliver, et al.. (2021). Using Domain-Specific Fingerprints Generated Through Neural Networks to Enhance Ligand-Based Virtual Screening. Journal of Chemical Information and Modeling. 61(2). 664–675. 20 indexed citations
6.
Humbeck, Lina, et al.. (2021). Discovery of an Unexpected Similarity in Ligand Binding between BRD4 and PPARγ. ACS Chemical Biology. 16(7). 1255–1265. 2 indexed citations
7.
Zacarı́as, Natalia V. Ortiz, Petra Hundehege, Oliver Koch, et al.. (2021). Piperazine squaric acid diamides, a novel class of allosteric P2X7 receptor antagonists. European Journal of Medicinal Chemistry. 226. 113838–113838. 10 indexed citations
8.
Bulk, Etmar, Zoltán Pethő, Thomas Budde, et al.. (2020). Co‐staining of KCa3.1 Channels in NSCLC Cells with a Small‐Molecule Fluorescent Probe and Antibody‐Based Indirect Immunofluorescence. ChemMedChem. 15(24). 2462–2469. 7 indexed citations
9.
Ehrt, Christiane, et al.. (2019). SCOT: Rethinking the classification of secondary structure elements. Bioinformatics. 36(8). 2417–2428. 7 indexed citations
10.
Morgen, Michael, Peter Sehr, Raphael R. Steimbach, et al.. (2019). Selective Inhibition of Histone Deacetylase 10: Hydrogen Bonding to the Gatekeeper Residue is Implicated. Journal of Medicinal Chemistry. 62(9). 4426–4443. 66 indexed citations
11.
Nogueira, Mauro S. & Oliver Koch. (2019). The Development of Target-Specific Machine Learning Models as Scoring Functions for Docking-Based Target Prediction. Journal of Chemical Information and Modeling. 59(3). 1238–1252. 39 indexed citations
12.
Ehrt, Christiane, et al.. (2019). Binding site characterization – similarity, promiscuity, and druggability. MedChemComm. 10(7). 1145–1159. 19 indexed citations
14.
Witte, Anna Kristina, et al.. (2018). Complete, Programmable Decoding of Oxidized 5-Methylcytosine Nucleobases in DNA by Chemoselective Blockage of Universal Transcription-Activator-Like Effector Repeats. Journal of the American Chemical Society. 140(18). 5904–5908. 11 indexed citations
17.
Ehrt, Christiane, et al.. (2018). A benchmark driven guide to binding site comparison: An exhaustive evaluation using tailor-made data sets (ProSPECCTs). PLoS Computational Biology. 14(11). e1006483–e1006483. 48 indexed citations
18.
Krüger, D., Adrian Glas, David Bier, et al.. (2017). Structure-Based Design of Non-natural Macrocyclic Peptides That Inhibit Protein–Protein Interactions. Journal of Medicinal Chemistry. 60(21). 8982–8988. 42 indexed citations
19.
Kriege, Nils M., et al.. (2017). Scaffold Hunter: a comprehensive visual analytics framework for drug discovery. Journal of Cheminformatics. 9(1). 28–28. 46 indexed citations
20.
Marquardt, Viktoria, Brigita Vı̄gante, Г. Дубурс, et al.. (2015). Design, synthesis and 3D-QSAR studies of novel 1,4-dihydropyridines as TGFβ/Smad inhibitors. European Journal of Medicinal Chemistry. 95. 249–266. 21 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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