Michael Reutlinger

1.8k total citations
40 papers, 990 citations indexed

About

Michael Reutlinger is a scholar working on Molecular Biology, Computational Theory and Mathematics and Organic Chemistry. According to data from OpenAlex, Michael Reutlinger has authored 40 papers receiving a total of 990 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Molecular Biology, 24 papers in Computational Theory and Mathematics and 9 papers in Organic Chemistry. Recurrent topics in Michael Reutlinger's work include Computational Drug Discovery Methods (24 papers), Machine Learning in Materials Science (7 papers) and Chemical Synthesis and Analysis (6 papers). Michael Reutlinger is often cited by papers focused on Computational Drug Discovery Methods (24 papers), Machine Learning in Materials Science (7 papers) and Chemical Synthesis and Analysis (6 papers). Michael Reutlinger collaborates with scholars based in Switzerland, Germany and United States. Michael Reutlinger's co-authors include Gisbert Schneider, Petra Schneider, Tiago Rodrigues, Daniel Reker, Bernd Kuhn, Neil R. Taylor, Martin Ståhl, Julian E. Fuchs, Markus Hartenfeller and Jan A. Hiss and has published in prestigious journals such as Angewandte Chemie International Edition, Nature Chemistry and Journal of Medicinal Chemistry.

In The Last Decade

Michael Reutlinger

39 papers receiving 969 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael Reutlinger Switzerland 17 604 598 207 190 178 40 990
Tuomo Kalliokoski Finland 14 509 0.8× 470 0.8× 208 1.0× 109 0.6× 118 0.7× 23 803
Markus Hartenfeller Switzerland 11 452 0.7× 539 0.9× 120 0.6× 125 0.7× 198 1.1× 15 685
Mathias J. Wawer Germany 17 698 1.2× 706 1.2× 217 1.0× 193 1.0× 136 0.8× 27 1.1k
Matthias Zentgraf Germany 11 511 0.8× 467 0.8× 129 0.6× 100 0.5× 265 1.5× 14 946
Zhixiong Zhao China 14 780 1.3× 610 1.0× 139 0.7× 200 1.1× 225 1.3× 30 1.1k
Ed Griffen United Kingdom 11 434 0.7× 482 0.8× 215 1.0× 97 0.5× 124 0.7× 15 775
Filip Miljković Germany 16 460 0.8× 554 0.9× 97 0.5× 91 0.5× 163 0.9× 43 806
Gavin Harper United Kingdom 11 745 1.2× 725 1.2× 216 1.0× 174 0.9× 126 0.7× 16 1.1k
Jeff W. Paslay United States 5 550 0.9× 356 0.6× 145 0.7× 78 0.4× 116 0.7× 6 971
Alexander Alanine Switzerland 16 753 1.2× 456 0.8× 363 1.8× 121 0.6× 117 0.7× 26 1.3k

Countries citing papers authored by Michael Reutlinger

Since Specialization
Citations

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

Fields of papers citing papers by Michael Reutlinger

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael Reutlinger

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Reutlinger. A scholar is included among the top collaborators of Michael Reutlinger 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 Michael Reutlinger. Michael Reutlinger 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.
Bassani, Davide, Michael Reutlinger, & Holger Fischer. (2025). Leveraging machine learning predicted confidence for boosting assay submission and decision-making efficiencies. European Journal of Medicinal Chemistry. 297. 117947–117947.
2.
Napoli, Joseph A., Michael Reutlinger, Peter Brandl, et al.. (2025). Multitask Deep Learning Models of Combined Industrial Absorption, Distribution, Metabolism, and Excretion Datasets to Improve Generalization. Molecular Pharmaceutics. 22(4). 1892–1900. 3 indexed citations
3.
Fraczkiewicz, Robert, Sergio Grimbs, Kai Sommer, et al.. (2024). Best of both worlds: An expansion of the state of the art pKa model with data from three industrial partners. Molecular Informatics. 43(10). e202400088–e202400088. 4 indexed citations
4.
Atz, Kenneth, Alex T. Müller, Andrea Anelli, et al.. (2024). Geometric deep learning-guided Suzuki reaction conditions assessment for applications in medicinal chemistry. RSC Medicinal Chemistry. 15(7). 2310–2321. 4 indexed citations
5.
Weber, Michaël, Jean‐Yves Wach, Anja Gundlfinger, et al.. (2023). Selective and brain-penetrant HCN1 inhibitors reveal links between synaptic integration, cortical function, and working memory. Cell chemical biology. 31(3). 577–592.e23. 8 indexed citations
6.
Broccatelli, Fabio, et al.. (2022). Benchmarking Accuracy and Generalizability of Four Graph Neural Networks Using Large In Vitro ADME Datasets from Different Chemical Spaces. Molecular Informatics. 41(8). e2100321–e2100321. 13 indexed citations
7.
Rudolph, M.G., Jason C. Cole, Michael Reutlinger, et al.. (2022). A high quality, industrial data set for binding affinity prediction: performance comparison in different early drug discovery scenarios. Journal of Computer-Aided Molecular Design. 36(10). 753–765. 25 indexed citations
8.
Guasch, Laura, Michael Reutlinger, Daniel Stoffler, & Moreno Wichert. (2021). Augmenting Chemical Space with DNA-encoded Library Technology and Machine Learning. CHIMIA International Journal for Chemistry. 75(1-2). 105–105. 6 indexed citations
9.
Reutlinger, Michael, et al.. (2016). Designing Multi‐target Compound Libraries with Gaussian Process Models. Molecular Informatics. 35(5). 192–198. 7 indexed citations
10.
Rupp, Matthias, Matthias R. Bauer, Rainer Wilcken, et al.. (2014). Machine Learning Estimates of Natural Product Conformational Energies. PLoS Computational Biology. 10(1). e1003400–e1003400. 30 indexed citations
11.
Reker, Daniel, Anna Maria Perna, Tiago Rodrigues, et al.. (2014). Revealing the macromolecular targets of complex natural products. Nature Chemistry. 6(12). 1072–1078. 102 indexed citations
12.
Reutlinger, Michael, Tiago Rodrigues, Petra Schneider, & Gisbert Schneider. (2014). Multi‐Objective Molecular De Novo Design by Adaptive Fragment Prioritization. Angewandte Chemie International Edition. 53(16). 4244–4248. 66 indexed citations
13.
Rodrigues, Tiago, N. Hauser, Daniel Reker, et al.. (2014). Multidimensional De Novo Design Reveals 5‐HT2B Receptor‐Selective Ligands. Angewandte Chemie International Edition. 54(5). 1551–1555. 38 indexed citations
14.
Reutlinger, Michael, et al.. (2013). Chemically Advanced Template Search (CATS) for Scaffold‐Hopping and Prospective Target Prediction for ‘Orphan’ Molecules. Molecular Informatics. 32(2). 133–138. 133 indexed citations
15.
Reutlinger, Michael, Tiago Rodrigues, Petra Schneider, & Gisbert Schneider. (2013). Combining On‐Chip Synthesis of a Focused Combinatorial Library with Computational Target Prediction Reveals Imidazopyridine GPCR Ligands. Angewandte Chemie International Edition. 53(2). 582–585. 53 indexed citations
16.
Reutlinger, Michael & Gisbert Schneider. (2012). Nonlinear dimensionality reduction and mapping of compound libraries for drug discovery. Journal of Molecular Graphics and Modelling. 34. 108–117. 51 indexed citations
17.
Spänkuch, Birgit, Tiago Rodrigues, Heiko Zettl, et al.. (2012). Drugs by Numbers: Reaction‐Driven De Novo Design of Potent and Selective Anticancer Leads. Angewandte Chemie International Edition. 52(17). 4676–4681. 20 indexed citations
18.
Geppert, Tim, Stefanie Bauer, Jan A. Hiss, et al.. (2011). Immunosuppressive Small Molecule Discovered by Structure‐Based Virtual Screening for Inhibitors of Protein–Protein Interactions. Angewandte Chemie International Edition. 51(1). 258–261. 33 indexed citations
19.
Reutlinger, Michael, Wolfgang Guba, Rainer E. Martin, et al.. (2011). Neighborhood‐Preserving Visualization of Adaptive Structure–Activity Landscapes: Application to Drug Discovery. Angewandte Chemie International Edition. 50(49). 11633–11636. 34 indexed citations
20.
Elmadfa, Ibrahim, et al.. (1989). Über die Transformation von γ-Tocopherol zu α-Tocopherol im tierischen Organismus; ein Generationsversuch an Ratten. Zeitschrift für Ernährungswissenschaft. 28(1). 36–48. 11 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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