Anke Mueller‐Fahrnow

1.4k total citations
8 papers, 219 citations indexed

About

Anke Mueller‐Fahrnow is a scholar working on Genetics, Computational Theory and Mathematics and Molecular Biology. According to data from OpenAlex, Anke Mueller‐Fahrnow has authored 8 papers receiving a total of 219 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Genetics, 5 papers in Computational Theory and Mathematics and 4 papers in Molecular Biology. Recurrent topics in Anke Mueller‐Fahrnow's work include Estrogen and related hormone effects (6 papers), Computational Drug Discovery Methods (5 papers) and Bioactive Compounds and Antitumor Agents (3 papers). Anke Mueller‐Fahrnow is often cited by papers focused on Estrogen and related hormone effects (6 papers), Computational Drug Discovery Methods (5 papers) and Bioactive Compounds and Antitumor Agents (3 papers). Anke Mueller‐Fahrnow collaborates with scholars based in France, Germany and Canada. Anke Mueller‐Fahrnow's co-authors include Ursula Egner, Jean‐Marie Wurtz, Nikolaus Heinrich, A.M. Edwards, Matthias Zwick, C.H. Arrowsmith, Adrian J. Carter, Oliver Kraemer, Dino Moras and Marc Ruff and has published in prestigious journals such as Journal of Medicinal Chemistry, Current Opinion in Biotechnology and Drug Discovery Today.

In The Last Decade

Anke Mueller‐Fahrnow

8 papers receiving 209 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Anke Mueller‐Fahrnow France 6 138 101 53 45 25 8 219
Jingle Xi China 7 145 1.1× 94 0.9× 81 1.5× 60 1.3× 73 2.9× 10 341
Carl Ouellet Canada 5 75 0.5× 102 1.0× 89 1.7× 26 0.6× 41 1.6× 6 234
Sherry Guo United States 5 151 1.1× 72 0.7× 28 0.5× 24 0.5× 68 2.7× 9 245
Lawrence Andrade United States 6 232 1.7× 26 0.3× 45 0.8× 16 0.4× 56 2.2× 10 344
F. Bouchoux France 10 122 0.9× 224 2.2× 79 1.5× 23 0.5× 33 1.3× 15 376
David T. Winn United States 9 166 1.2× 68 0.7× 106 2.0× 11 0.2× 41 1.6× 11 282
R. Van Ginckel Belgium 12 199 1.4× 209 2.1× 57 1.1× 10 0.2× 55 2.2× 23 418
Jon J. Hangeland United States 10 241 1.7× 32 0.3× 163 3.1× 22 0.5× 25 1.0× 17 396
Robert Dally United States 7 86 0.6× 25 0.2× 83 1.6× 24 0.5× 28 1.1× 11 249
Raquel Garcı́a-Nieto Spain 9 135 1.0× 37 0.4× 98 1.8× 10 0.2× 26 1.0× 10 324

Countries citing papers authored by Anke Mueller‐Fahrnow

Since Specialization
Citations

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

Fields of papers citing papers by Anke Mueller‐Fahrnow

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Anke Mueller‐Fahrnow. 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 Anke Mueller‐Fahrnow. The network helps show where Anke Mueller‐Fahrnow may publish in the future.

Co-authorship network of co-authors of Anke Mueller‐Fahrnow

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

All Works

8 of 8 papers shown
1.
Carter, Adrian J., Oliver Kraemer, Matthias Zwick, et al.. (2019). Target 2035: probing the human proteome. Drug Discovery Today. 24(11). 2111–2115. 82 indexed citations
2.
Becker, Andreas, et al.. (2016). Public–Private Partnerships in Lead Discovery: Overview and Case Studies. Archiv der Pharmazie. 349(9). 692–697. 8 indexed citations
3.
Egner, Ursula, Nikolaus Heinrich, Marc Ruff, et al.. (2001). Different ligands–different receptor conformations: Modeling of the hERα LBD in complex with agonists and antagonists. Medicinal Research Reviews. 21(6). 523–539. 25 indexed citations
4.
Egner, Ursula, et al.. (2000). ChemInform Abstract: 7α,15α‐Ethano Bridged Steroids. Synthesis and Progesterone Receptor Interaction.. ChemInform. 31(2). 1 indexed citations
5.
Bringmann, Peter, et al.. (1999). Investigation of the binding interactions of progesterone using muteins of the human progesterone receptor ligand binding domain designed on the basis of a three-dimensional protein model. Biochimica et Biophysica Acta (BBA) - Protein Structure and Molecular Enzymology. 1429(2). 391–400. 19 indexed citations
6.
Mueller‐Fahrnow, Anke & Ursula Egner. (1999). Ligand-binding domain of estrogen receptors. Current Opinion in Biotechnology. 10(6). 550–556. 31 indexed citations
7.
Wurtz, Jean‐Marie, Ursula Egner, Nikolaus Heinrich, Dino Moras, & Anke Mueller‐Fahrnow. (1998). Three-Dimensional Models of Estrogen Receptor Ligand Binding Domain Complexes, Based on Related Crystal Structures and Mutational and Structure−Activity Relationship Data. Journal of Medicinal Chemistry. 41(11). 1803–1814. 52 indexed citations
8.
Cleve, Arwed, K.-H. Fritzemeier, Nikolaus Heinrich, et al.. (1996). ChemInform Abstract: 11β‐Aryl Steroids in the Androstene Series. The Role of the 11. beta.‐Region in Steroid Progesterone Receptor Interaction.. ChemInform. 27(20). 1 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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