Stéphanie Baud

2.0k citations
51 papers · 1.5k · 1 hit paper · h-index 17

Impact in

Papers in

Stéphanie Baud

47 papers receiving 1.5k citations

Stéphanie Baud's Hit Papers

Machine-learning methods for ligand–protein molecular docking 2021 · 205 citations
2050+1+3Years since publication50100150200

Peers

Stéphanie Baud
Comparison fields: 5 of 128
  • Cell Biology 304
  • Immunology and Allergy 109
  • Biomaterials 220
  • Cancer Research 204
  • Molecular Biology 777
Replace Rudolph L. Juliano with:
Rudolph L. Juliano United States
Christine Unger Austria
Wen Chen China
Srikumar M. Raja United States
Steven R. Van Doren United States
Sigrid A. Langhans United States
Jos Joore Netherlands
Cathy Staedel France
Yingnan Zhang China
Audrey F. Adcock United States
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Citations per field
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Citations per year

Countries citing papers authored by Stéphanie Baud

Since Specialization
Citations

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

Fields of papers citing papers by Stéphanie Baud

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Stéphanie Baud, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Stéphanie Baud Line = papers co-authored together Stéphanie Baud links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 51 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2006306
2 2018283
3
Machine-learning methods for ligand–protein molecular docking
Hit paper breakdown →
2021205
4 201389
5 201755
6 201249
7 201042
8 201640
9 200434
10 201430
11 201226
12 201626
13 200623
14 201822
15 201522
16 201521
17 202017
18 201114
19 201314
20 200213

About Stéphanie Baud

Stéphanie Baud is a scholar working on Molecular Biology, Cancer Research, Genetics, Organic Chemistry and Radiology, Nuclear Medicine and Imaging, having authored 51 papers that have together received 1.5k indexed citations. Recurring topics across this work include Protease and Inhibitor Mechanisms (15 papers), Connective tissue disorders research (13 papers), Glycosylation and Glycoproteins Research (12 papers), Protein Structure and Dynamics (7 papers), Monoclonal and Polyclonal Antibodies Research (6 papers), Cell Adhesion Molecules Research (5 papers), Computational Drug Discovery Methods (5 papers) and Carbohydrate Chemistry and Synthesis (4 papers). The work is most often cited by research in Cell Biology (304 citations), Immunology and Allergy (109 citations), Biomaterials (220 citations), Cancer Research (204 citations) and Molecular Biology (777 citations). Stéphanie Baud has collaborated with scholars based in France, Italy and Germany. Frequent co-authors include Régis Pomès, Sarah Rauscher, Ming Miao, Manuel Dauchez, Luiz Angelo Steffenel, Stéphane Brézillon, Nicolas Belloy, Sylvie Ricard‐Blum, Davide Vigetti and Hideto Watanabe. Their work appears in journals such as Scientific Reports, Frontiers in Cell and Developmental Biology, Matrix Biology, Journal of Biological Chemistry and Physical Review B.

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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