Peter Gedeck

2.8k total citations
54 papers, 1.7k citations indexed

About

Peter Gedeck is a scholar working on Molecular Biology, Computational Theory and Mathematics and Organic Chemistry. According to data from OpenAlex, Peter Gedeck has authored 54 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Molecular Biology, 19 papers in Computational Theory and Mathematics and 11 papers in Organic Chemistry. Recurrent topics in Peter Gedeck's work include Computational Drug Discovery Methods (19 papers), Machine Learning in Materials Science (9 papers) and Free Radicals and Antioxidants (7 papers). Peter Gedeck is often cited by papers focused on Computational Drug Discovery Methods (19 papers), Machine Learning in Materials Science (9 papers) and Free Radicals and Antioxidants (7 papers). Peter Gedeck collaborates with scholars based in Switzerland, Germany and United Kingdom. Peter Gedeck's co-authors include Christian Krämer, Anna Vulpetti, Tuomo Kalliokoski, Bernhard Rohde, Christian Bartels, Timothy Clark, Markus Meuwly, Siegfried Schneider, Peter Willett and Martin Gosling and has published in prestigious journals such as Journal of the American Chemical Society, PLoS ONE and The Journal of Physical Chemistry B.

In The Last Decade

Peter Gedeck

52 papers receiving 1.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Peter Gedeck Switzerland 23 904 811 360 282 189 54 1.7k
Christian Krämer Switzerland 25 1.2k 1.4× 985 1.2× 450 1.3× 266 0.9× 361 1.9× 75 2.3k
Gregory A. Ross United States 17 1.2k 1.3× 507 0.6× 294 0.8× 290 1.0× 118 0.6× 26 2.0k
Brett A. Tounge United States 19 709 0.8× 392 0.5× 327 0.9× 286 1.0× 99 0.5× 33 1.6k
Yoshifumi Fukunishi Japan 25 1.2k 1.3× 529 0.7× 335 0.9× 157 0.6× 319 1.7× 116 1.9k
Joseph W. Kaus United States 8 1.6k 1.8× 745 0.9× 348 1.0× 586 2.1× 163 0.9× 8 2.7k
José S. Duca United States 25 1.1k 1.2× 817 1.0× 373 1.0× 380 1.3× 55 0.3× 56 2.1k
E. Prabhu Raman United States 21 2.0k 2.2× 697 0.9× 463 1.3× 360 1.3× 281 1.5× 29 3.0k
Chaya S. Rapp United States 12 2.0k 2.2× 623 0.8× 471 1.3× 378 1.3× 175 0.9× 14 2.8k
Stefan Senger United Kingdom 15 1.1k 1.2× 969 1.2× 307 0.9× 373 1.3× 93 0.5× 28 1.9k
Pieter F. W. Stouten United States 20 1.4k 1.6× 696 0.9× 306 0.8× 313 1.1× 85 0.4× 54 2.1k

Countries citing papers authored by Peter Gedeck

Since Specialization
Citations

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

Fields of papers citing papers by Peter Gedeck

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Peter Gedeck

This figure shows the co-authorship network connecting the top 25 collaborators of Peter Gedeck. A scholar is included among the top collaborators of Peter Gedeck 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 Peter Gedeck. Peter Gedeck 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.
Bruce, Peter, et al.. (2024). Statistics for Data Science and Analytics. 1 indexed citations
2.
Kenett, Ron S., Shelemyahu Zacks, & Peter Gedeck. (2023). Industrial Statistics. 4 indexed citations
3.
Clark, Alex M., et al.. (2021). Using Machine Learning to Parse Chemical Mixture Descriptions. ACS Omega. 6(34). 22400–22409. 3 indexed citations
4.
Clark, Alex M., Leah McEwen, Peter Gedeck, & Barry A. Bunin. (2019). Capturing mixture composition: an open machine-readable format for representing mixed substances. Journal of Cheminformatics. 11(1). 33–33. 12 indexed citations
5.
Lu, Yipin, Shankara Anand, William A. Shirley, et al.. (2019). Prediction of pKa Using Machine Learning Methods with Rooted Topological Torsion Fingerprints: Application to Aliphatic Amines. Journal of Chemical Information and Modeling. 59(11). 4706–4719. 30 indexed citations
6.
Krämer, Christian, et al.. (2013). Deriving Static Atomic Multipoles from the Electrostatic Potential. Journal of Chemical Information and Modeling. 53(12). 3410–3417. 23 indexed citations
7.
Kalliokoski, Tuomo, Christian Krämer, Anna Vulpetti, & Peter Gedeck. (2013). Comparability of Mixed IC50 Data – A Statistical Analysis. PLoS ONE. 8(4). e61007–e61007. 227 indexed citations
8.
Krämer, Christian, Peter Gedeck, & Markus Meuwly. (2012). Atomic multipoles: Electrostatic potential fit, local reference axis systems, and conformational dependence. Journal of Computational Chemistry. 33(20). 1673–1688. 57 indexed citations
9.
McCarren, Patrick, Gregory R. Bebernitz, Peter Gedeck, et al.. (2011). Avoidance of the Ames test liability for aryl–amines via computation. Bioorganic & Medicinal Chemistry. 19(10). 3173–3182. 22 indexed citations
10.
Bhalay, Gurdip, Mohammed Shahid Akhlaq, David Beer, et al.. (2011). Design and synthesis of a library of chemokine antagonists. Bioorganic & Medicinal Chemistry Letters. 21(21). 6249–6252. 4 indexed citations
11.
Beattie, David T., Steven J. Charlton, Bernard Cuenoud, et al.. (2010). A physical properties based approach for the exploration of a 4-hydroxybenzothiazolone series of β2-adrenoceptor agonists as inhaled long-acting bronchodilators. Bioorganic & Medicinal Chemistry Letters. 20(17). 5302–5307. 19 indexed citations
12.
Gedeck, Peter, Christian Krämer, & Peter Ertl. (2010). Computational Analysis of Structure–Activity Relationships. Progress in medicinal chemistry. 49. 113–160. 22 indexed citations
13.
Beattie, David T., Zarin Brown, Steven J. Charlton, et al.. (2009). Synthesis and evaluation of two series of 4′-aza-carbocyclic nucleosides as adenosine A2A receptor agonists. Bioorganic & Medicinal Chemistry Letters. 20(3). 1219–1224. 9 indexed citations
14.
Stansfeld, Phillip J., Alessandro Grottesi, Zara A. Sands, et al.. (2008). Insight into the Mechanism of Inactivation and pH Sensitivity in Potassium Channels from Molecular Dynamics Simulations. Biochemistry. 47(28). 7414–7422. 47 indexed citations
15.
Stansfeld, Phillip J., Peter Gedeck, Martin Gosling, et al.. (2007). Drug block of the hERG potassium channel: Insight from modeling. Proteins Structure Function and Bioinformatics. 68(2). 568–580. 86 indexed citations
16.
Trifilieff, Alexandre, Thomas H. Keller, Neil J. Press, et al.. (2005). CGH2466, a combined adenosine receptor antagonist, p38 mitogen‐activated protein kinase and phosphodiesterase type 4 inhibitor with potent in vitro and in vivo anti‐inflammatory activities. British Journal of Pharmacology. 144(7). 1002–1010. 16 indexed citations
17.
Press, Neil J., Thomas H. Keller, Pamela Tranter, et al.. (2004). New Highly Potent and Selective Adenosine A3 Receptor Antagonists. Current Topics in Medicinal Chemistry. 4(8). 863–870. 14 indexed citations
18.
Beer, David, David Bentley, Ian Bruce, et al.. (2004). Long-chain formoterol analogues: an investigation into the effect of increasing amino-substituent chain length on the β2-adrenoceptor activity. Bioorganic & Medicinal Chemistry Letters. 14(18). 4705–4710. 32 indexed citations
19.
Gedeck, Peter & Peter Willett. (2001). Visual and computational analysis of structure–activity relationships in high-throughput screening data. Current Opinion in Chemical Biology. 5(4). 389–395. 29 indexed citations
20.
Gedeck, Peter, Torsten Schindler, Alexander Alex, & Timothy Clark. (2000). New Multicentre Point Charge Models for Molecular Electrostatic Potentials from Semiempirical M0-Calculations. Journal of Molecular Modeling. 6(6). 452–466. 6 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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