Natália Aniceto

534 total citations
19 papers, 370 citations indexed

About

Natália Aniceto is a scholar working on Molecular Biology, Computational Theory and Mathematics and Oncology. According to data from OpenAlex, Natália Aniceto has authored 19 papers receiving a total of 370 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 8 papers in Computational Theory and Mathematics and 5 papers in Oncology. Recurrent topics in Natália Aniceto's work include Computational Drug Discovery Methods (8 papers), Drug Transport and Resistance Mechanisms (3 papers) and Ubiquitin and proteasome pathways (3 papers). Natália Aniceto is often cited by papers focused on Computational Drug Discovery Methods (8 papers), Drug Transport and Resistance Mechanisms (3 papers) and Ubiquitin and proteasome pathways (3 papers). Natália Aniceto collaborates with scholars based in Portugal, United Kingdom and Germany. Natália Aniceto's co-authors include Andreas Bender, Taravat Ghafourian, Alex A. Freitas, Rita C. Guedes, G Ulrich‐Merzenich, Michael P. Menden, Anna Hendrika Cornelia Vlot, Lars Carlsson, José Morais and Isidro Cortés‐Ciriano and has published in prestigious journals such as International Journal of Molecular Sciences, Molecules and Nutrients.

In The Last Decade

Natália Aniceto

19 papers receiving 366 citations

Peers

Natália Aniceto

CTM

SPECT

Tyler Peryea United States

Zheng Du China

Jadson Castro Gertrudes Brazil

Tiago Alves de Oliveira Brazil

Tevfik Kizilören United Kingdom

José Liñares-Blanco Spain

Andreas Maunz Switzerland

Heather L. Ciallella United States

Christof H. Schwab Germany

Tyler Peryea United States

Natália Aniceto

218 ×1.2

108 ×0.7

CTM

40 ×0.9

37 ×0.9

33 ×0.9

SPECT

Citations per year, relative to Natália Aniceto Natália Aniceto (= 1×) peers Tyler Peryea

Countries citing papers authored by Natália Aniceto

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Natália Aniceto

This figure shows the co-authorship network connecting the top 25 collaborators of Natália Aniceto. A scholar is included among the top collaborators of Natália Aniceto 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 Natália Aniceto. Natália Aniceto is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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19 of 19 papers shown

Proença, Carina, Alberto N. Araújo, Marisa Freitas, et al.. (2025). Flavonoids as Potential Modulators of Pancreatic Lipase Catalytic Activity. Pharmaceutics. 17(2). 163–163. 2 indexed citations

Aniceto, Natália, et al.. (2023). Using Machine Learning and Molecular Docking to Leverage Urease Inhibition Data for Virtual Screening. International Journal of Molecular Sciences. 24(9). 8180–8180. 4 indexed citations

Aniceto, Natália, Vanda Marques, Joana D. Amaral, et al.. (2022). Harnessing Protein-Ligand Interaction Fingerprints to Predict New Scaffolds of RIPK1 Inhibitors. Molecules. 27(15). 4718–4718. 2 indexed citations

Carvalho, Andreia Neves, et al.. (2022). Revisiting Proteasome Inhibitors: Molecular Underpinnings of Their Development, Mechanisms of Resistance and Strategies to Overcome Anti-Cancer Drug Resistance. Molecules. 27(7). 2201–2201. 17 indexed citations

Aniceto, Natália, Vasco D. B. Bonifácio, Rita C. Guedes, & Nuno Martinho. (2022). Exploring the Chemical Space of Urease Inhibitors to Extract Meaningful Trends and Drivers of Activity. Journal of Chemical Information and Modeling. 62(15). 3535–3550. 10 indexed citations

Rocha, Sónia, Natália Aniceto, Rita C. Guedes, et al.. (2022). An In Silico and an In Vitro Inhibition Analysis of Glycogen Phosphorylase by Flavonoids, Styrylchromones, and Pyrazoles. Nutrients. 14(2). 306–306. 13 indexed citations

Rocha, Sónia, Mariana Lucas, Vera L. M. Silva, et al.. (2021). Pyrazoles as novel protein tyrosine phosphatase 1B (PTP1B) inhibitors: An in vitro and in silico study. International Journal of Biological Macromolecules. 181. 1171–1182. 25 indexed citations

Aniceto, Natália, et al.. (2021). Exploring EZH2-Proteasome Dual-Targeting Drug Discovery through a Computational Strategy to Fight Multiple Myeloma. Molecules. 26(18). 5574–5574. 4 indexed citations

Marques, Vanda, Marta B. Afonso, Dean G. Brown, et al.. (2020). Phenotypic high-throughput screening platform identifies novel chemotypes for necroptosis inhibition. Cell Death Discovery. 6(1). 6–6. 16 indexed citations

10.

Aniceto, Natália, et al.. (2019). Chemical Patterns of Proteasome Inhibitors: Lessons Learned from Two Decades of Drug Design. International Journal of Molecular Sciences. 20(21). 5326–5326. 12 indexed citations

11.

Vlot, Anna Hendrika Cornelia, Natália Aniceto, Michael P. Menden, G Ulrich‐Merzenich, & Andreas Bender. (2019). Applying synergy metrics to combination screening data: agreements, disagreements and pitfalls. Drug Discovery Today. 24(12). 2286–2298. 70 indexed citations

12.

Svensson, Fredrik, Natália Aniceto, Ulf Norinder, et al.. (2018). Conformal Regression for Quantitative Structure–Activity Relationship Modeling—Quantifying Prediction Uncertainty. Journal of Chemical Information and Modeling. 58(5). 1132–1140. 44 indexed citations

13.

Aniceto, Natália, et al.. (2017). Population Approach to Efavirenz Therapy. Journal of Pharmaceutical Sciences. 106(10). 3161–3166. 7 indexed citations

14.

Aniceto, Natália, Alex A. Freitas, Andreas Bender, & Taravat Ghafourian. (2016). A novel applicability domain technique for mapping predictive reliability across the chemical space of a QSAR: reliability-density neighbourhood. Journal of Cheminformatics. 8(1). 103 indexed citations

15.

Michaelis, Martin, Florian Rothweiler, Mario Wurglics, et al.. (2016). Substrate-specific effects of pirinixic acid derivatives on ABCB1-mediated drug transport. Oncotarget. 7(10). 11664–11676. 5 indexed citations

16.

Aniceto, Natália, Alex A. Freitas, Andreas Bender, & Taravat Ghafourian. (2016). Simultaneous Prediction of four ATP‐binding Cassette Transporters’ Substrates Using Multi‐label QSAR. Molecular Informatics. 35(10). 514–528. 10 indexed citations

17.

Paixão, Paulo, Natália Aniceto, Luís F. Gouveia, & José Morais. (2014). Prediction of Drug Distribution in Rat and Humans Using an Artificial Neural Networks Ensemble and a PBPK Model. Pharmaceutical Research. 31(12). 3313–3322. 12 indexed citations

18.

Lanhers, Marie‐Claire, M. Socha, Mehdi Yemloul, et al.. (2013). The antitumor effects of an arsthinol–cyclodextrin complex in a heterotopic mouse model of glioma. European Journal of Pharmaceutics and Biopharmaceutics. 85(3). 560–568. 5 indexed citations

19.

Paixão, Paulo, Natália Aniceto, Luís F. Gouveia, & José Morais. (2013). Tissue-to-blood distribution coefficients in the rat: Utility for estimation of the volume of distribution in man. European Journal of Pharmaceutical Sciences. 50(3-4). 526–543. 9 indexed citations

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.

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