Fernando D. Suvire

48 total papers · 760 total citations
43 papers, 683 citations indexed

About

Fernando D. Suvire is a scholar working on Molecular Biology, Organic Chemistry and Spectroscopy. According to data from OpenAlex, Fernando D. Suvire has authored 43 papers receiving a total of 683 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Molecular Biology, 20 papers in Organic Chemistry and 9 papers in Spectroscopy. Recurrent topics in Fernando D. Suvire's work include Computational Drug Discovery Methods (8 papers), Protein Structure and Dynamics (7 papers) and Receptor Mechanisms and Signaling (6 papers). Fernando D. Suvire is often cited by papers focused on Computational Drug Discovery Methods (8 papers), Protein Structure and Dynamics (7 papers) and Receptor Mechanisms and Signaling (6 papers). Fernando D. Suvire collaborates with scholars based in Argentina, Spain and Canada. Fernando D. Suvire's co-authors include Ricardo D. Enriz, Sebastián A. Andújar, Diego Cortés, Nuria Cabedo, Almudena Bermejo, Emilio Angelina, Héctor A. Baldoni, Noureddine El Aouad, Susana Zacchino and Nélida M. Peruchena and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Medicinal Chemistry and Tetrahedron.

In The Last Decade

Fernando D. Suvire

43 papers receiving 680 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Fernando D. Suvire	344	299	101	85	82	43	683
Héctor A. Baldoni	165 0.5×	431 1.4×	92 0.9×	90 1.1×	162 2.0×	39	710
Stephen P. East	314 0.9×	271 0.9×	43 0.4×	44 0.5×	47 0.6×	22	690
Gerald Zapata‐Torres	181 0.5×	291 1.0×	48 0.5×	45 0.5×	122 1.5×	48	758
Kunisi S. Venkatasubban	212 0.6×	248 0.8×	25 0.2×	87 1.0×	134 1.6×	39	575
Antônia Tavares do Amaral	289 0.8×	276 0.9×	68 0.7×	29 0.3×	44 0.5×	52	681
Gustavo Seabra	149 0.4×	342 1.1×	61 0.6×	60 0.7×	95 1.2×	41	718
Krzysztof Z. Łączkowski	515 1.5×	172 0.6×	58 0.6×	49 0.6×	85 1.0×	60	773
N. R. Jena	196 0.6×	429 1.4×	41 0.4×	95 1.1×	37 0.5×	40	780
Troy Wymore	129 0.4×	421 1.4×	31 0.3×	43 0.5×	58 0.7×	47	726
Boris Weiss‐López	268 0.8×	219 0.7×	21 0.2×	58 0.7×	157 1.9×	59	696

Countries citing papers authored by Fernando D. Suvire

Since Specialization

Citations

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

Fields of papers citing papers by Fernando D. Suvire

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fernando D. Suvire. 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 Fernando D. Suvire. The network helps show where Fernando D. Suvire may publish in the future.

Co-authorship network of co-authors of Fernando D. Suvire

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

All Works

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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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