Jochen Sieg
Impact in
-
- Computational Drug Discovery Methods
-
- Protein Structure and Dynamics
- Bioinformatics and Genomic Networks
- Genetics, Bioinformatics, and Biomedical Research
- Chemical Synthesis and Analysis
- vaccines and immunoinformatics approaches
Papers in
-
- Enzyme Structure and Function 4
- Machine Learning in Materials Science 4
-
- Protein Structure and Dynamics 5
- Genetics, Bioinformatics, and Biomedical Research 1
- Metabolomics and Mass Spectrometry Studies 1
- Microbial Metabolic Engineering and Bioproduction 1
- Co-authors
- Matthias Rarey (6 shared papers)Florian Flachsenberg (3 shared papers)Katrin Stierand (1 shared paper)Patrick Penner (1 shared paper)Konrad Diedrich (1 shared paper)Christiane Ehrt (1 shared paper)Miriam Mathea (3 shared papers)Andrea Volkamer (1 shared paper)
- Journals
- Journal of Chemical Information and Modeling (4 papers)Bioinformatics (1 paper)Faraday Discussions (1 paper)Proteins Structure Function and Bioinformatics (1 paper)Briefings in Bioinformatics (1 paper)
- Partner nations
- Germany
In The Last Decade
Jochen Sieg
9 papers receiving 401 citations
Peers
Comparison fields: 5 of 93
- Computational Theory and Mathematics 223
- Molecular Biology 231
- Materials Chemistry 131
- Pharmacology 36
- Pharmacology 11
Countries citing papers authored by Jochen Sieg
This map shows the geographic impact of Jochen Sieg'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 Jochen Sieg with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jochen Sieg more than expected).
Fields of papers citing papers by Jochen Sieg
This network shows the impact of papers produced by Jochen Sieg. 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 Jochen Sieg. The network helps show where Jochen Sieg may publish in the future.
Co-authors
The 20 scholars most cited alongside Jochen Sieg, 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 | 2019 | 219 | |
| 2 | 2022 | 134 | |
| 3 | 2024 | 20 | |
| 4 | 2024 | 17 | |
| 5 | 2018 | 6 | |
| 6 | 2023 | 5 | |
| 7 | 2022 | 3 | |
| 8 | 2024 | 3 | |
| 9 | 2023 | 3 |
About Jochen Sieg
Jochen Sieg is a scholar working on Materials Chemistry, Molecular Biology, Computational Theory and Mathematics, Control and Systems Engineering and Artificial Intelligence, having authored 9 papers that have together received 410 indexed citations. Recurring topics across this work include Protein Structure and Dynamics (5 papers), Computational Drug Discovery Methods (4 papers), Enzyme Structure and Function (4 papers), Machine Learning in Materials Science (4 papers), Genetics, Bioinformatics, and Biomedical Research (1 paper), Metabolomics and Mass Spectrometry Studies (1 paper), Microbial Metabolic Engineering and Bioproduction (1 paper) and Adversarial Robustness in Machine Learning (1 paper). The work is most often cited by research in Computational Theory and Mathematics (223 citations), Molecular Biology (231 citations), Materials Chemistry (131 citations), Pharmacology (36 citations) and Pharmacology (11 citations). Jochen Sieg has collaborated with scholars based in Germany. Frequent co-authors include Matthias Rarey, Florian Flachsenberg, Katrin Stierand, Patrick Penner, Konrad Diedrich, Christiane Ehrt, Miriam Mathea, Andrea Volkamer, Christian Feldmann and Philipp Eiden. Their work appears in journals such as Journal of Chemical Information and Modeling, Bioinformatics, Faraday Discussions, Proteins Structure Function and Bioinformatics and Briefings in Bioinformatics.
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.