Thomas Seidel
Impact in
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- Computational Drug Discovery Methods
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- Protein Structure and Dynamics
- Receptor Mechanisms and Signaling
- Chemical Synthesis and Analysis
Papers in
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- Protein Structure and Dynamics 9
- Chemical Synthesis and Analysis 8
- Receptor Mechanisms and Signaling 6
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- Computational Drug Discovery Methods 29
- Co-authors
- Thierry Langer (33 shared papers)Arthur Garon (11 shared papers)Oliver Wieder (6 shared papers)Stefan M. Kohlbacher (5 shared papers)Mélaine A. Kuenemann (2 shared papers)Marcus Wieder (9 shared papers)Gerhard Wolber (4 shared papers)Ugo Perricone (6 shared papers)
In The Last Decade
Thomas Seidel
52 papers receiving 1.1k citations
Thomas Seidel's Hit Papers
Peers
Comparison fields: 5 of 123
- Computational Theory and Mathematics 609
- Molecular Biology 552
- Pharmacology 61
- Materials Chemistry 262
- Pharmacology 80
Countries citing papers authored by Thomas Seidel
This map shows the geographic impact of Thomas Seidel'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 Thomas Seidel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas Seidel more than expected).
Fields of papers citing papers by Thomas Seidel
This network shows the impact of papers produced by Thomas Seidel. 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 Thomas Seidel. The network helps show where Thomas Seidel may publish in the future.
Co-authors
The 25 scholars most cited alongside Thomas Seidel, 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 54 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | A compact review of molecular property prediction with graph neural networks Hit paper breakdown → | 2020 | 348 |
| 2 | 2010 | 101 | |
| 3 | 2020 | 70 | |
| 4 | 2019 | 63 | |
| 5 | 2017 | 56 | |
| 6 | 2018 | 49 | |
| 7 | 2018 | 42 | |
| 8 | 2020 | 26 | |
| 9 | 2015 | 26 | |
| 10 | 2016 | 26 | |
| 11 | 2021 | 25 | |
| 12 | 2017 | 23 | |
| 13 | 2009 | 22 | |
| 14 | 2019 | 20 | |
| 15 | 2021 | 17 | |
| 16 | 2016 | 16 | |
| 17 | 2018 | 15 | |
| 18 | 2023 | 14 | |
| 19 | 2018 | 14 | |
| 20 | 2022 | 12 |
About Thomas Seidel
Thomas Seidel is a scholar working on Molecular Biology, Computational Theory and Mathematics, Materials Chemistry, Pharmacology and Cellular and Molecular Neuroscience, having authored 54 papers that have together received 1.1k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (29 papers), Protein Structure and Dynamics (9 papers), Chemical Synthesis and Analysis (8 papers), Neuroscience and Neuropharmacology Research (6 papers), Receptor Mechanisms and Signaling (6 papers), Analytical Chemistry and Chromatography (6 papers), Monoclonal and Polyclonal Antibodies Research (5 papers) and Advanced Combustion Engine Technologies (5 papers). The work is most often cited by research in Computational Theory and Mathematics (609 citations), Molecular Biology (552 citations), Pharmacology (61 citations), Materials Chemistry (262 citations) and Pharmacology (80 citations). Thomas Seidel has collaborated with scholars based in Austria, Italy and Germany. Frequent co-authors include Thierry Langer, Arthur Garon, Oliver Wieder, Stefan M. Kohlbacher, Mélaine A. Kuenemann, Marcus Wieder, Gerhard Wolber, Ugo Perricone, Giulio Poli and Doris A. Schuetz. Their work appears in journals such as Journal of Chemical Information and Modeling, Molecular Informatics, SAE technical papers on CD-ROM/SAE technical paper series, MTZ - Motortechnische Zeitschrift and Journal of Cheminformatics.
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.