Kostas Papadopoulos
- Computational Theory and Mathematics top 1%
- Molecular Biology
- Materials Chemistry
- Biomedical Engineering
- Pharmacology
- Co-authors
- Atanas PatronovOla EngkvistChristian MargreitterHongming ChenChristian TyrchanThomas BlaschkeJosep Arús‐PousJon Paul Janet
- Topics
- Computational Drug Discovery Methods (7 papers)Machine Learning in Materials Science (5 papers)Protein Structure and Dynamics (3 papers)
- Journals
- The Journal of PathologyBioorganic & Medicinal ChemistryJournal of Chemical Information and Modeling
- Partner nations
- SwedenUnited KingdomGreece
In The Last Decade
Kostas Papadopoulos
10 papers receiving 507 citations
Hit Papers
Peers
Comparison fields: 5 of 75
- Computational Theory and Mathematics 364
- Molecular Biology 315
- Materials Chemistry 261
- Biomedical Engineering 63
- Pharmacology 34
Countries citing papers authored by Kostas Papadopoulos
This map shows the geographic impact of Kostas Papadopoulos'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 Kostas Papadopoulos with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kostas Papadopoulos more than expected).
Fields of papers citing papers by Kostas Papadopoulos
This network shows the impact of papers produced by Kostas Papadopoulos. 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 Kostas Papadopoulos. The network helps show where Kostas Papadopoulos may publish in the future.
Co-authorship network of co-authors of Kostas Papadopoulos
This figure shows the co-authorship network connecting the top 25 collaborators of Kostas Papadopoulos. A scholar is included among the top collaborators of Kostas Papadopoulos based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Kostas Papadopoulos. Kostas Papadopoulos is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 48 | |
| 2 | 14 | |
| 3 | 1 | |
| 4 | 33 | |
| 5 | 17 | |
| 6 | 39 | |
| 7 | 18 | |
| 8 | 48 | |
| 9 | REINVENT 2.0: An AI Tool for De Novo Drug Designbreakdown → | 263 |
| 10 | 41 |
About Kostas Papadopoulos
Kostas Papadopoulos is a scholar working on Computational Theory and Mathematics, Environmental Chemistry and Molecular Biology, having authored 10 papers that have together received 522 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (7 papers), Machine Learning in Materials Science (5 papers) and Protein Structure and Dynamics (3 papers). The work is most often cited by research in Computational Theory and Mathematics (364 citations), Materials Chemistry (261 citations) and Molecular Biology (315 citations). Kostas Papadopoulos has collaborated with scholars based in Sweden, United Kingdom and Greece. Frequent co-authors include Atanas Patronov, Ola Engkvist, Christian Margreitter, Hongming Chen, Christian Tyrchan, Thomas Blaschke, Josep Arús‐Pous, Jon Paul Janet, Jeff Guo and Esben Jannik Bjerrum. Their work appears in journals such as The Journal of Pathology, Bioorganic & Medicinal Chemistry and Journal of Chemical Information and Modeling.
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.