Tim Hempel
Impact in
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- Computational Drug Discovery Methods
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- SARS-CoV-2 and COVID-19 Research
- COVID-19 Clinical Research Studies
Papers in
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- Protein Structure and Dynamics 3
- Pharmacological Receptor Mechanisms and Effects 2
- Cancer therapeutics and mechanisms 1
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- Computational Drug Discovery Methods 4
- Co-authors
- Frank Noé (9 shared papers)Simon Olsson (4 shared papers)Stefan Pöhlmann (3 shared papers)Markus Hoffmann (3 shared papers)Lluı́s Raich (3 shared papers)Marc E. Rothenberg (1 shared paper)Andrea M. Klingler (1 shared paper)Nurit P. Azouz (1 shared paper)
- Journals
- Chemical Science (2 papers)Nature Communications (2 papers)Structure (1 paper)Current Opinion in Structural Biology (1 paper)Proceedings of the National Academy of Sciences (1 paper)
- Partner nations
- GermanyUnited StatesSweden
In The Last Decade
Tim Hempel
9 papers receiving 186 citations
Peers
Comparison fields: 5 of 57
- Computational Theory and Mathematics 58
- Infectious Diseases 68
- Molecular Biology 103
- Acoustics and Ultrasonics 1
- Statistical and Nonlinear Physics 9
Countries citing papers authored by Tim Hempel
This map shows the geographic impact of Tim Hempel'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 Tim Hempel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tim Hempel more than expected).
Fields of papers citing papers by Tim Hempel
This network shows the impact of papers produced by Tim Hempel. 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 Tim Hempel. The network helps show where Tim Hempel may publish in the future.
Co-authors
The 25 scholars most cited alongside Tim Hempel, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 76 | |
| 2 | 2018 | 40 | |
| 3 | 2022 | 15 | |
| 4 | 2021 | 15 | |
| 5 | 2021 | 14 | |
| 6 | 2020 | 10 | |
| 7 | 2022 | 9 | |
| 8 | 2023 | 5 | |
| 9 | Introduction to Markov state modeling with the PyEMMA software — v1.0 | 2018 | 3 |
| 10 | 2025 | 0 |
About Tim Hempel
Tim Hempel is a scholar working on Molecular Biology, Computational Theory and Mathematics, Infectious Diseases, Spectroscopy and Materials Chemistry, having authored 10 papers that have together received 187 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (4 papers), Protein Structure and Dynamics (3 papers), SARS-CoV-2 and COVID-19 Research (2 papers), Machine Learning in Materials Science (2 papers), Mass Spectrometry Techniques and Applications (2 papers), Pharmacological Receptor Mechanisms and Effects (2 papers), Cancer therapeutics and mechanisms (1 paper) and Various Chemistry Research Topics (1 paper). The work is most often cited by research in Computational Theory and Mathematics (58 citations), Infectious Diseases (68 citations), Molecular Biology (103 citations), Acoustics and Ultrasonics (1 citation) and Statistical and Nonlinear Physics (9 citations). Tim Hempel has collaborated with scholars based in Germany, United States and Sweden. Frequent co-authors include Frank Noé, Simon Olsson, Stefan Pöhlmann, Markus Hoffmann, Lluı́s Raich, Marc E. Rothenberg, Andrea M. Klingler, Nurit P. Azouz, Christoph Wehmeyer and Brooke E. Husic. Their work appears in journals such as Chemical Science, Nature Communications, Structure, Current Opinion in Structural Biology and Proceedings of the National Academy of Sciences.
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.