Natália Aniceto
Impact in
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- Computational Drug Discovery Methods
Papers in
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- Ubiquitin and proteasome pathways 3
- Protein Tyrosine Phosphatases 2
- Protein Degradation and Inhibitors 2
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- Computational Drug Discovery Methods 8
- Co-authors
- Andreas Bender (4 shared papers)Taravat Ghafourian (3 shared papers)Alex A. Freitas (2 shared papers)G Ulrich‐Merzenich (1 shared paper)Anna Hendrika Cornelia Vlot (1 shared paper)Rita C. Guedes (10 shared papers)Michael P. Menden (1 shared paper)Isidro Cortés‐Ciriano (1 shared paper)
- Journals
- Molecules (3 papers)International Journal of Molecular Sciences (2 papers)Journal of Chemical Information and Modeling (2 papers)Journal of Pharmaceutical Sciences (1 paper)Cell Death Discovery (1 paper)
- Partner nations
- PortugalUnited KingdomGermany
In The Last Decade
Natália Aniceto
19 papers receiving 366 citations
Peers
Comparison fields: 5 of 94
- Computational Theory and Mathematics 148
- Toxicology 10
- Molecular Biology 176
- Spectroscopy 36
- Pharmacology 19
Countries citing papers authored by Natália Aniceto
This map shows the geographic impact of Natália Aniceto'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 Natália Aniceto with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Natália Aniceto more than expected).
Fields of papers citing papers by Natália Aniceto
This network shows the impact of papers produced by Natália Aniceto. 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 Natália Aniceto. The network helps show where Natália Aniceto may publish in the future.
Co-authors
The 25 scholars most cited alongside Natália Aniceto, 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 | 2016 | 103 | |
| 2 | 2019 | 70 | |
| 3 | 2018 | 44 | |
| 4 | 2021 | 25 | |
| 5 | 2022 | 17 | |
| 6 | 2020 | 16 | |
| 7 | 2022 | 13 | |
| 8 | 2014 | 12 | |
| 9 | 2019 | 12 | |
| 10 | 2016 | 10 | |
| 11 | 2022 | 10 | |
| 12 | 2013 | 9 | |
| 13 | 2017 | 7 | |
| 14 | 2013 | 5 | |
| 15 | 2016 | 5 | |
| 16 | 2023 | 4 | |
| 17 | 2021 | 4 | |
| 18 | 2022 | 2 | |
| 19 | 2025 | 2 |
About Natália Aniceto
Natália Aniceto is a scholar working on Molecular Biology, Computational Theory and Mathematics, Oncology, Pharmacology and Infectious Diseases, having authored 19 papers that have together received 370 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (8 papers), Drug Transport and Resistance Mechanisms (3 papers), Ubiquitin and proteasome pathways (3 papers), Protein Tyrosine Phosphatases (2 papers), Pharmacogenetics and Drug Metabolism (2 papers), Machine Learning in Materials Science (2 papers), Protein Degradation and Inhibitors (2 papers) and Microbial Applications in Construction Materials (2 papers). The work is most often cited by research in Computational Theory and Mathematics (148 citations), Toxicology (10 citations), Molecular Biology (176 citations), Spectroscopy (36 citations) and Pharmacology (19 citations). Natália Aniceto has collaborated with scholars based in Portugal, United Kingdom and Germany. Frequent co-authors include Andreas Bender, Taravat Ghafourian, Alex A. Freitas, G Ulrich‐Merzenich, Anna Hendrika Cornelia Vlot, Rita C. Guedes, Michael P. Menden, Isidro Cortés‐Ciriano, José Morais and Fredrik Svensson. Their work appears in journals such as Molecules, International Journal of Molecular Sciences, Journal of Chemical Information and Modeling, Journal of Pharmaceutical Sciences and Cell Death Discovery.
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.