Matteo Pavan
Impact in
- Physiology top 5%
- Adenosine and Purinergic Signaling
-
- Computational Drug Discovery Methods
Papers in
-
- Protein Structure and Dynamics 8
- Receptor Mechanisms and Signaling 6
- Pharmacological Receptor Mechanisms and Effects 5
- RNA and protein synthesis mechanisms 3
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- Computational Drug Discovery Methods 12
- Co-authors
- Stefano Moro (28 shared papers)Mattia Sturlese (18 shared papers)Davide Bassani (20 shared papers)Giovanni Bolcato (8 shared papers)Maicol Bissaro (4 shared papers)Valentina Gandin (3 shared papers)Girolamo Cirrincione (3 shared papers)Daniela Carbone (3 shared papers)
- Journals
- International Journal of Molecular Sciences (6 papers)Journal of Enzyme Inhibition and Medicinal Chemistry (3 papers)Pharmaceuticals (3 papers)European Journal of Medicinal Chemistry (2 papers)ChemMedChem (2 papers)
- Partner nations
- ItalyUnited StatesCzechia
In The Last Decade
Matteo Pavan
32 papers receiving 528 citations
Peers
Comparison fields: 5 of 72
- Physiology 58
- Computational Theory and Mathematics 171
- Infectious Diseases 154
- Molecular Biology 303
- Organic Chemistry 122
Countries citing papers authored by Matteo Pavan
This map shows the geographic impact of Matteo Pavan'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 Matteo Pavan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matteo Pavan more than expected).
Fields of papers citing papers by Matteo Pavan
This network shows the impact of papers produced by Matteo Pavan. 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 Matteo Pavan. The network helps show where Matteo Pavan may publish in the future.
Co-authors
The 25 scholars most cited alongside Matteo Pavan, 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 34 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 69 | |
| 2 | 2021 | 56 | |
| 3 | 2020 | 49 | |
| 4 | 2022 | 41 | |
| 5 | 2023 | 30 | |
| 6 | 2023 | 30 | |
| 7 | 2023 | 23 | |
| 8 | 2022 | 22 | |
| 9 | 2022 | 19 | |
| 10 | 2022 | 18 | |
| 11 | 2023 | 17 | |
| 12 | 2023 | 16 | |
| 13 | 2021 | 16 | |
| 14 | 2021 | 14 | |
| 15 | 2022 | 13 | |
| 16 | 2022 | 13 | |
| 17 | 2023 | 10 | |
| 18 | 2023 | 9 | |
| 19 | 2024 | 9 | |
| 20 | 2023 | 9 |
About Matteo Pavan
Matteo Pavan is a scholar working on Molecular Biology, Computational Theory and Mathematics, Physiology, Infectious Diseases and Organic Chemistry, having authored 34 papers that have together received 537 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (12 papers), Adenosine and Purinergic Signaling (8 papers), Protein Structure and Dynamics (8 papers), SARS-CoV-2 and COVID-19 Research (7 papers), Receptor Mechanisms and Signaling (6 papers), Pharmacological Receptor Mechanisms and Effects (5 papers), RNA and protein synthesis mechanisms (3 papers) and Click Chemistry and Applications (3 papers). The work is most often cited by research in Physiology (58 citations), Computational Theory and Mathematics (171 citations), Infectious Diseases (154 citations), Molecular Biology (303 citations) and Organic Chemistry (122 citations). Matteo Pavan has collaborated with scholars based in Italy, United States and Czechia. Frequent co-authors include Stefano Moro, Mattia Sturlese, Davide Bassani, Giovanni Bolcato, Maicol Bissaro, Valentina Gandin, Girolamo Cirrincione, Daniela Carbone, Michele De Franco and Stella Cascioferro. Their work appears in journals such as International Journal of Molecular Sciences, Journal of Enzyme Inhibition and Medicinal Chemistry, Pharmaceuticals, European Journal of Medicinal Chemistry and ChemMedChem.
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.