Tim Hempel
Impact in
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- SARS-CoV-2 and COVID-19 Research
- COVID-19 Clinical Research Studies
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- Computational Drug Discovery Methods
Papers in
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- Protein Structure and Dynamics 3
- Pharmacological Receptor Mechanisms and Effects 2
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- Computational Drug Discovery Methods 4
- Co-authors
- Frank Noé (9 shared papers)Simon Olsson (4 shared papers)Lluı́s Raich (3 shared papers)Markus Hoffmann (3 shared papers)Stefan Pöhlmann (3 shared papers)Nurit P. Azouz (1 shared paper)Andrea M. Klingler (1 shared paper)Marc E. Rothenberg (1 shared paper)
- Journals
- Nature Communications (2 papers)Chemical Science (2 papers)Proceedings of the National Academy of Sciences (1 paper)Current Opinion in Structural Biology (1 paper)Journal of Chemical Theory and Computation (1 paper)
- Partner nations
- GermanyUnited StatesUnited Kingdom
In The Last Decade
Tim Hempel
10 papers receiving 195 citations
Peers
Comparison fields: 5 of 60
- Infectious Diseases 65
- Computational Theory and Mathematics 55
- Molecular Biology 104
- Acoustics and Ultrasonics 1
- Materials Chemistry 35
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 | 80 | |
| 2 | 2018 | 40 | |
| 3 | 2021 | 18 | |
| 4 | 2021 | 15 | |
| 5 | 2022 | 15 | |
| 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 | 1 |
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 196 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (4 papers), Protein Structure and Dynamics (3 papers), Pharmacological Receptor Mechanisms and Effects (2 papers), Machine Learning in Materials Science (2 papers), SARS-CoV-2 and COVID-19 Research (2 papers), Mass Spectrometry Techniques and Applications (2 papers), Trypanosoma species research and implications (1 paper) and Markov Chains and Monte Carlo Methods (1 paper). The work is most often cited by research in Infectious Diseases (65 citations), Computational Theory and Mathematics (55 citations), Molecular Biology (104 citations), Acoustics and Ultrasonics (1 citation) and Materials Chemistry (35 citations). Tim Hempel has collaborated with scholars based in Germany, United States and United Kingdom. Frequent co-authors include Frank Noé, Simon Olsson, Lluı́s Raich, Markus Hoffmann, Stefan Pöhlmann, Nurit P. Azouz, Andrea M. Klingler, Marc E. Rothenberg, Martin K. Scherer and Brooke E. Husic. Their work appears in journals such as Nature Communications, Chemical Science, Proceedings of the National Academy of Sciences, Current Opinion in Structural Biology and Journal of Chemical Theory and Computation.
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.