Marc Bianciotto

47 total papers · 962 total citations
16 papers, 465 citations indexed

About

Marc Bianciotto is a scholar working on Molecular Biology, Computational Theory and Mathematics and Materials Chemistry. According to data from OpenAlex, Marc Bianciotto has authored 16 papers receiving a total of 465 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Molecular Biology, 9 papers in Computational Theory and Mathematics and 6 papers in Materials Chemistry. Recurrent topics in Marc Bianciotto's work include Computational Drug Discovery Methods (9 papers), Protein Structure and Dynamics (8 papers) and Machine Learning in Materials Science (5 papers). Marc Bianciotto is often cited by papers focused on Computational Drug Discovery Methods (9 papers), Protein Structure and Dynamics (8 papers) and Machine Learning in Materials Science (5 papers). Marc Bianciotto collaborates with scholars based in France, Germany and Spain. Marc Bianciotto's co-authors include Jean‐Claude Barthelat, Alain Vigroux, Maxime Langevin, Francesco Luigi Gervasio, Hervé Minoux, Rebecca C. Wade, Daria B. Kokh, Matthias Dreyer, Maryse Lowinski and François Vallée and has published in prestigious journals such as Journal of the American Chemical Society, Journal of Biological Chemistry and The Journal of Chemical Physics.

In The Last Decade

Marc Bianciotto

15 papers receiving 452 citations

Author Peers

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

Author						Papers	Cites
Marc Bianciotto	350	188	109	72	34	16	465
Paulius Mikulskis	383 1.1×	184 1.0×	97 0.9×	73 1.0×	79 2.3×	15	520
Martin Almlöf	411 1.2×	170 0.9×	79 0.7×	56 0.8×	57 1.7×	9	536
Johan Åqvist	390 1.1×	126 0.7×	79 0.7×	112 1.6×	42 1.2×	9	518
Mattia Bernetti	388 1.1×	170 0.9×	86 0.8×	31 0.4×	40 1.2×	22	471
Robert S. DeWitte	293 0.8×	209 1.1×	104 1.0×	90 1.3×	38 1.1×	9	434
Alexander S. Bayden	325 0.9×	106 0.6×	76 0.7×	76 1.1×	23 0.7×	14	467
Mike J. Bodkin	324 0.9×	203 1.1×	78 0.7×	37 0.5×	44 1.3×	15	414
Camilo Velez‐Vega	330 0.9×	108 0.6×	70 0.6×	26 0.4×	60 1.8×	19	432
Bernhard Baum	295 0.8×	195 1.0×	86 0.8×	71 1.0×	17 0.5×	9	415
Lance M. Westerhoff	341 1.0×	196 1.0×	138 1.3×	97 1.3×	92 2.7×	15	505

Countries citing papers authored by Marc Bianciotto

Since Specialization

Citations

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

Fields of papers citing papers by Marc Bianciotto

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marc Bianciotto

This figure shows the co-authorship network connecting the top 25 collaborators of Marc Bianciotto. A scholar is included among the top collaborators of Marc Bianciotto 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 Marc Bianciotto. Marc Bianciotto 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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