Stefan Doerr
Impact in
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- Computational Drug Discovery Methods
- Molecular Biology top 10%
- Protein Structure and Dynamics
- Receptor Mechanisms and Signaling
- RNA and protein synthesis mechanisms
- Machine Learning in Bioinformatics
Papers in
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- Protein Structure and Dynamics 10
- Receptor Mechanisms and Signaling 3
- Protein purification and stability 1
- Gene Regulatory Network Analysis 1
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- Machine Learning in Materials Science 6
- Enzyme Structure and Function 2
- Co-authors
- Gianni De Fabritiis (12 shared papers)Frank Noé (3 shared papers)Gérard Martinez (2 shared papers)José Jiménez-Luna (2 shared papers)Alexander Rose (1 shared paper)M J Harvey (2 shared papers)Nuria Plattner (1 shared paper)Toni Giorgino (2 shared papers)
- Journals
- Journal of Chemical Theory and Computation (5 papers)Journal of Chemical Information and Modeling (4 papers)Scientific Reports (1 paper)The Journal of Physical Chemistry B (1 paper)Nature Chemistry (1 paper)
- Partner nations
- SpainUnited StatesGermany
In The Last Decade
Stefan Doerr
13 papers receiving 1.6k citations
Stefan Doerr's Hit Papers
Peers
Comparison fields: 5 of 122
- Computational Theory and Mathematics 551
- Molecular Biology 1.1k
- Materials Chemistry 495
- Spectroscopy 130
- Cellular and Molecular Neuroscience 89
Countries citing papers authored by Stefan Doerr
This map shows the geographic impact of Stefan Doerr'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 Stefan Doerr with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stefan Doerr more than expected).
Fields of papers citing papers by Stefan Doerr
This network shows the impact of papers produced by Stefan Doerr. 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 Stefan Doerr. The network helps show where Stefan Doerr may publish in the future.
Co-authors
The 25 scholars most cited alongside Stefan Doerr, 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 | DeepSite: protein-binding site predictor using 3D-convolutional neural networks Hit paper breakdown → | 2017 | 527 |
| 2 | 2016 | 306 | |
| 3 | 2017 | 254 | |
| 4 | 2021 | 172 | |
| 5 | 2014 | 136 | |
| 6 | 2019 | 66 | |
| 7 | 2023 | 54 | |
| 8 | 2018 | 45 | |
| 9 | 2024 | 37 | |
| 10 | 2017 | 25 | |
| 11 | 2022 | 15 | |
| 12 | 2024 | 11 | |
| 13 | 2022 | 6 |
About Stefan Doerr
Stefan Doerr is a scholar working on Molecular Biology, Materials Chemistry, Computational Theory and Mathematics, Spectroscopy and Cell Biology, having authored 13 papers that have together received 1.7k indexed citations. Recurring topics across this work include Protein Structure and Dynamics (10 papers), Computational Drug Discovery Methods (6 papers), Machine Learning in Materials Science (6 papers), Receptor Mechanisms and Signaling (3 papers), Mass Spectrometry Techniques and Applications (3 papers), Enzyme Structure and Function (2 papers), Protein purification and stability (1 paper) and Gene Regulatory Network Analysis (1 paper). The work is most often cited by research in Computational Theory and Mathematics (551 citations), Molecular Biology (1.1k citations), Materials Chemistry (495 citations), Spectroscopy (130 citations) and Cellular and Molecular Neuroscience (89 citations). Stefan Doerr has collaborated with scholars based in Spain, United States and Germany. Frequent co-authors include Gianni De Fabritiis, Frank Noé, Gérard Martinez, José Jiménez-Luna, Alexander Rose, M J Harvey, Nuria Plattner, Toni Giorgino, Raimondas Galvelis and Maciej Majewski. Their work appears in journals such as Journal of Chemical Theory and Computation, Journal of Chemical Information and Modeling, Scientific Reports, The Journal of Physical Chemistry B and Nature Chemistry.
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.