Ugo Perricone

740 total citations
40 papers, 545 citations indexed

About

Ugo Perricone is a scholar working on Molecular Biology, Computational Theory and Mathematics and Organic Chemistry. According to data from OpenAlex, Ugo Perricone has authored 40 papers receiving a total of 545 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Molecular Biology, 23 papers in Computational Theory and Mathematics and 5 papers in Organic Chemistry. Recurrent topics in Ugo Perricone's work include Computational Drug Discovery Methods (23 papers), Protein Structure and Dynamics (7 papers) and Receptor Mechanisms and Signaling (5 papers). Ugo Perricone is often cited by papers focused on Computational Drug Discovery Methods (23 papers), Protein Structure and Dynamics (7 papers) and Receptor Mechanisms and Signaling (5 papers). Ugo Perricone collaborates with scholars based in Italy, Austria and France. Ugo Perricone's co-authors include Anna Maria Almerico, Thierry Langer, Marcus Wieder, Thomas Seidel, Alessandro Padova, Marco Tutone, Annamaria Martorana, Stefan Boresch, Antonino Lauria and Patrizia Diana and has published in prestigious journals such as Scientific Reports, Biochemical and Biophysical Research Communications and International Journal of Molecular Sciences.

In The Last Decade

Ugo Perricone

38 papers receiving 537 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ugo Perricone Italy 15 316 188 134 53 40 40 545
Hayarpi Torosyan United States 9 350 1.1× 215 1.1× 104 0.8× 36 0.7× 42 1.1× 11 576
Flavio Ballante United States 15 330 1.0× 161 0.9× 165 1.2× 55 1.0× 36 0.9× 25 519
Atefeh Saadabadi Finland 7 205 0.6× 155 0.8× 89 0.7× 37 0.7× 32 0.8× 13 470
Ji-Xia Ren China 10 235 0.7× 192 1.0× 136 1.0× 35 0.7× 33 0.8× 21 451
Kathryn Loving United States 6 292 0.9× 182 1.0× 75 0.6× 47 0.9× 46 1.1× 7 441
Kalaimathy Singaravelu Finland 8 288 0.9× 153 0.8× 75 0.6× 29 0.5× 30 0.8× 17 527
Letícia C. Assis Brazil 10 233 0.7× 256 1.4× 80 0.6× 43 0.8× 43 1.1× 20 502
Sanna Niinivehmas Finland 13 286 0.9× 205 1.1× 123 0.9× 32 0.6× 74 1.9× 22 517
Georgios Leonis Greece 14 286 0.9× 118 0.6× 99 0.7× 103 1.9× 64 1.6× 31 575
Selvaraman Nagamani India 13 195 0.6× 177 0.9× 63 0.5× 34 0.6× 50 1.3× 44 380

Countries citing papers authored by Ugo Perricone

Since Specialization
Citations

This map shows the geographic impact of Ugo Perricone'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 Ugo Perricone with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ugo Perricone more than expected).

Fields of papers citing papers by Ugo Perricone

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ugo Perricone. 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 Ugo Perricone. The network helps show where Ugo Perricone may publish in the future.

Co-authorship network of co-authors of Ugo Perricone

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

All Works

20 of 20 papers shown
1.
Perricone, Ugo, et al.. (2025). APBIO: bioactive profiling of air pollutants through inferred bioactivity signatures and prediction of novel target interactions. Journal of Cheminformatics. 17(1). 13–13. 1 indexed citations
2.
Perricone, Ugo, Patrizia Rubino, Angela Bonura, et al.. (2025). ENO1/Hsp70 Interaction Domains: In Silico and In Vitro Insight for a Putative Therapeutic Target in Cancer. ACS Omega. 10(5). 5036–5046.
3.
Spinello, Angelo, Ugo Perricone, Florent Barbault, et al.. (2024). Resolving the Structure of a Guanine Quadruplex in TMPRSS2 Messenger RNA by Circular Dichroism and Molecular Modeling. Chemistry - A European Journal. 30(71). e202403572–e202403572. 2 indexed citations
4.
Montalbano, Mauro, et al.. (2023). Glypican-3 (GPC-3) Structural Analysis and Cargo in Serum Small Extracellular Vesicles of Hepatocellular Carcinoma Patients. International Journal of Molecular Sciences. 24(13). 10922–10922. 12 indexed citations
5.
Perricone, Ugo, et al.. (2022). EMBER—Embedding Multiple Molecular Fingerprints for Virtual Screening. International Journal of Molecular Sciences. 23(4). 2156–2156. 10 indexed citations
6.
Perricone, Ugo, et al.. (2022). KUALA: a machine learning-driven framework for kinase inhibitors repositioning. Scientific Reports. 12(1). 17877–17877. 6 indexed citations
7.
Coronnello, Claudia, et al.. (2021). Support Vector Machine as a Supervised Learning for the Prioritization of Novel Potential SARS-CoV-2 Main Protease Inhibitors. International Journal of Molecular Sciences. 22(14). 7714–7714. 17 indexed citations
8.
Rosa, Maria De, Daniela Carbone, Barbara Parrino, et al.. (2020). Dynamic‐shared Pharmacophore Approach as Tool to Design New Allosteric PRC2 Inhibitors, Targeting EED Binding Pocket. Molecular Informatics. 40(2). e2000148–e2000148. 1 indexed citations
9.
Perricone, Ugo, et al.. (2020). Convolutional architectures for virtual screening. BMC Bioinformatics. 21(S8). 310–310. 14 indexed citations
10.
Perricone, Ugo, et al.. (2020). Targeting SARS‐CoV‐2 RBD Interface: a Supervised Computational Data‐Driven Approach to Identify Potential Modulators. ChemMedChem. 15(20). 1921–1931. 6 indexed citations
11.
Rosa, Maria De, et al.. (2019). In Silico Insights towards the Identification of NLRP3 Druggable Hot Spots. International Journal of Molecular Sciences. 20(20). 4974–4974. 22 indexed citations
12.
Perricone, Ugo, Barbara Parrino, Stella Cascioferro, et al.. (2018). An overview of recent molecular dynamics applications as medicinal chemistry tools for the undruggable site challenge. MedChemComm. 9(6). 920–936. 42 indexed citations
13.
Perricone, Ugo, Marcus Wieder, Thomas Seidel, Thierry Langer, & Alessandro Padova. (2018). The Use of Dynamic Pharmacophore in Computer-Aided Hit Discovery: A Case Study. Methods in molecular biology. 1824. 317–333. 1 indexed citations
14.
Indelicato, Serena, David Bongiorno, Valentina Calabrese, et al.. (2017). Micelles, Rods, Liposomes, and Other Supramolecular Surfactant Aggregates: Computational Approaches. Interdisciplinary Sciences Computational Life Sciences. 9(3). 392–405. 13 indexed citations
15.
Lauria, Antonino, et al.. (2016). Drugs Polypharmacology by In Silico Methods: New Opportunities in Drug Discovery. Current Pharmaceutical Design. 22(21). 3073–3081. 12 indexed citations
16.
Martorana, Annamaria, Ugo Perricone, & Antonino Lauria. (2016). The Repurposing of Old Drugs or Unsuccessful Lead Compounds by in Silico Approaches: New Advances and Perspectives. Current Topics in Medicinal Chemistry. 16(19). 2088–2106. 21 indexed citations
17.
Wieder, Marcus, Ugo Perricone, Thomas Seidel, & Thierry Langer. (2016). Pharmacophore Models Derived from Molecular Dynamics Simulations of Protein-Ligand Complexes: A Case Study. Natural Product Communications. 11(10). 1499–1504. 4 indexed citations
18.
Wieder, Marcus, Ugo Perricone, Thomas Seidel, Stefan Boresch, & Thierry Langer. (2016). Comparing pharmacophore models derived from crystal structures and from molecular dynamics simulations. Monatshefte für Chemie - Chemical Monthly. 147(3). 553–563. 15 indexed citations
19.
Wieder, Marcus, Ugo Perricone, Stefan Boresch, Thomas Seidel, & Thierry Langer. (2016). Evaluating the stability of pharmacophore features using molecular dynamics simulations. Biochemical and Biophysical Research Communications. 470(3). 685–689. 21 indexed citations
20.
Tutone, Marco, et al.. (2016). Design, synthesis and preliminary evaluation of dopamine-amino acid conjugates as potential D1 dopaminergic modulators. European Journal of Medicinal Chemistry. 124. 435–444. 13 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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