Eva Nittinger
Impact in
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- Computational Drug Discovery Methods
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- Protein Structure and Dynamics
- Chemical Synthesis and Analysis
- Protein Degradation and Inhibitors
Papers in
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- Protein Structure and Dynamics 13
- Protein Degradation and Inhibitors 3
- Chemical Synthesis and Analysis 3
- Machine Learning in Bioinformatics 2
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- Computational Drug Discovery Methods 21
- Co-authors
- Matthias Rarey (10 shared papers)Agnes Meyder (6 shared papers)Florian Flachsenberg (4 shared papers)Stefan Bietz (7 shared papers)Gudrun Lange (5 shared papers)Christian Tyrchan (12 shared papers)Andrea Volkamer (3 shared papers)Robert J. Klein (4 shared papers)
In The Last Decade
Eva Nittinger
26 papers receiving 886 citations
Peers
Comparison fields: 5 of 116
- Computational Theory and Mathematics 428
- Molecular Biology 543
- Materials Chemistry 263
- Pharmacology 38
- Pharmacology 65
Countries citing papers authored by Eva Nittinger
This map shows the geographic impact of Eva Nittinger'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 Eva Nittinger with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eva Nittinger more than expected).
Fields of papers citing papers by Eva Nittinger
This network shows the impact of papers produced by Eva Nittinger. 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 Eva Nittinger. The network helps show where Eva Nittinger may publish in the future.
Co-authors
The 25 scholars most cited alongside Eva Nittinger, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 28 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 194 | |
| 2 | 2017 | 182 | |
| 3 | 2017 | 68 | |
| 4 | 2021 | 63 | |
| 5 | 2017 | 59 | |
| 6 | 2015 | 43 | |
| 7 | 2022 | 41 | |
| 8 | 2021 | 39 | |
| 9 | 2018 | 35 | |
| 10 | 2019 | 29 | |
| 11 | 2021 | 26 | |
| 12 | 2023 | 20 | |
| 13 | 2022 | 14 | |
| 14 | 2021 | 11 | |
| 15 | 2024 | 9 | |
| 16 | 2024 | 9 | |
| 17 | 2016 | 9 | |
| 18 | 2024 | 8 | |
| 19 | 2023 | 8 | |
| 20 | 2024 | 7 |
About Eva Nittinger
Eva Nittinger is a scholar working on Molecular Biology, Computational Theory and Mathematics, Materials Chemistry, Biomedical Engineering and Rehabilitation, having authored 28 papers that have together received 901 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (21 papers), Machine Learning in Materials Science (13 papers), Protein Structure and Dynamics (13 papers), Enzyme Structure and Function (8 papers), Protein Degradation and Inhibitors (3 papers), Innovative Microfluidic and Catalytic Techniques Innovation (3 papers), Chemical Synthesis and Analysis (3 papers) and Machine Learning in Bioinformatics (2 papers). The work is most often cited by research in Computational Theory and Mathematics (428 citations), Molecular Biology (543 citations), Materials Chemistry (263 citations), Pharmacology (38 citations) and Pharmacology (65 citations). Eva Nittinger has collaborated with scholars based in Sweden, Germany and Finland. Frequent co-authors include Matthias Rarey, Agnes Meyder, Florian Flachsenberg, Stefan Bietz, Gudrun Lange, Christian Tyrchan, Andrea Volkamer, Robert J. Klein, Katrin Stierand and Konrad Diedrich. Their work appears in journals such as Journal of Cheminformatics, Journal of Chemical Information and Modeling, ACS Omega, Journal of Computer-Aided Molecular Design and Nucleic Acids Research.
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.