Candida Manelfi
Impact in
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- Computational Drug Discovery Methods
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- SARS-CoV-2 and COVID-19 Research
Papers in
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- Protein Structure and Dynamics 3
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- Computational Drug Discovery Methods 11
- Co-authors
- Andrea R. Beccari (17 shared papers)Carmine Talarico (15 shared papers)Alessandro Pedretti (7 shared papers)Silvia Gervasoni (6 shared papers)Giulio Vistoli (7 shared papers)Erik Lindahl (2 shared papers)Carmen Cerchia (4 shared papers)Marica Gemei (3 shared papers)
In The Last Decade
Candida Manelfi
19 papers receiving 288 citations
Peers
Comparison fields: 5 of 77
- Computational Theory and Mathematics 146
- Infectious Diseases 95
- Pharmacology 22
- Molecular Biology 143
- Complementary and alternative medicine 14
Countries citing papers authored by Candida Manelfi
This map shows the geographic impact of Candida Manelfi'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 Candida Manelfi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Candida Manelfi more than expected).
Fields of papers citing papers by Candida Manelfi
This network shows the impact of papers produced by Candida Manelfi. 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 Candida Manelfi. The network helps show where Candida Manelfi may publish in the future.
Co-authors
The 25 scholars most cited alongside Candida Manelfi, 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 | 2022 | 51 | |
| 2 | 2020 | 45 | |
| 3 | 2021 | 35 | |
| 4 | 2020 | 25 | |
| 5 | 2018 | 24 | |
| 6 | 2020 | 20 | |
| 7 | 2018 | 19 | |
| 8 | 2021 | 15 | |
| 9 | 2021 | 14 | |
| 10 | 2022 | 12 | |
| 11 | 2024 | 8 | |
| 12 | 2023 | 7 | |
| 13 | 2022 | 6 | |
| 14 | 2022 | 3 | |
| 15 | 2023 | 2 | |
| 16 | 2023 | 2 | |
| 17 | 2022 | 2 | |
| 18 | 2020 | 2 | |
| 19 | 2024 | 1 |
About Candida Manelfi
Candida Manelfi is a scholar working on Molecular Biology, Computational Theory and Mathematics, Infectious Diseases, Materials Chemistry and Cardiology and Cardiovascular Medicine, having authored 19 papers that have together received 293 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (11 papers), SARS-CoV-2 and COVID-19 Research (7 papers), Machine Learning in Materials Science (3 papers), Protein Structure and Dynamics (3 papers), Microbial Natural Products and Biosynthesis (2 papers), Neuropeptides and Animal Physiology (2 papers), PARP inhibition in cancer therapy (2 papers) and Viral Infections and Immunology Research (2 papers). The work is most often cited by research in Computational Theory and Mathematics (146 citations), Infectious Diseases (95 citations), Pharmacology (22 citations), Molecular Biology (143 citations) and Complementary and alternative medicine (14 citations). Candida Manelfi has collaborated with scholars based in Italy, Germany and Sweden. Frequent co-authors include Andrea R. Beccari, Carmine Talarico, Alessandro Pedretti, Silvia Gervasoni, Giulio Vistoli, Erik Lindahl, Carmen Cerchia, Marica Gemei, Philip Gribbon and Andrea Zaliani. Their work appears in journals such as International Journal of Molecular Sciences, Journal of Cheminformatics, ACS Pharmacology & Translational Science, Virus Research and Scientific Reports.
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.