Jérôme Eberhardt

7.2k citations
19 papers · 3.9k indexed · 1 hit paper · h-index 11

Jérôme Eberhardt

17 papers receiving 3.9k citations

Hit Papers

AutoDock Vina 1.2.0: New Docking Methods, Expanded Force ...3.6k202120262022202410002.0k3.0k

Peers

Jérôme Eberhardt
Comparison fields: 5 of 147
  • Computational Theory and Mathematics 856
  • Toxicology 109
  • Pharmacology 263
  • Molecular Biology 2.0k
  • Organic Chemistry 740
Replace Andreas F. Tillack with:
Andreas F. Tillack United States
Sebastian Salentin Germany
V. Joachim Haupt Germany
Melissa F. Adasme Germany
Diogo Santos‐Martins United States
Keun Woo Lee South Korea
Rajendra Kumar Sweden
Sargis Dallakyan United States
Xuan-Yu Meng China
Luciana Scotti Brazil
Jérôme Eberhardt relative to Andreas F. Tillack United States Andreas F. Tillack's profile →
Citations per field
00.5×1.5×
Andreas F. Tillack · 1×
Citations per year

Countries citing papers authored by Jérôme Eberhardt

Since Specialization
Citations

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

Fields of papers citing papers by Jérôme Eberhardt

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Jérôme Eberhardt, 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 Jérôme Eberhardt Line = papers co-authored together Jérôme Eberhardt links everyone, so they are left out of the graph.

All Works

19 of 19 papers shown
#Work
1 20252
2 20250
3 20250
4 20241
5 202337
6 202312
7 202325
8 20234
9
AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindingsbreakdown →
20213596
10 202040
11 20206
12 20208
13 201930
14 201953
15 201914
16 201913
17 201812
18 20182
19 201437

About Jérôme Eberhardt

Jérôme Eberhardt is a scholar working on Computational Theory and Mathematics, Toxicology and Molecular Biology, having authored 19 papers that have together received 3.9k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (10 papers), Protein Structure and Dynamics (9 papers), Enzyme Structure and Function (4 papers), Machine Learning in Materials Science (3 papers), Drug Transport and Resistance Mechanisms (2 papers), Estrogen and related hormone effects (2 papers), Glycosylation and Glycoproteins Research (1 paper) and Bioactive Compounds and Antitumor Agents (1 paper). The work is most often cited by research in Computational Theory and Mathematics (856 citations), Toxicology (109 citations) and Pharmacology (263 citations). Jérôme Eberhardt has collaborated with scholars based in United States, Switzerland and Germany. Frequent co-authors include Stefano Forli, Diogo Santos‐Martins, Andreas F. Tillack, Janani Durairaj, Xavier Robin, Annick Dejaegere, Torsten Schwede, Leonardo Solis-Vasquez, Andreas Koch and Giulia Bianco. Their work appears in journals such as Proteins Structure Function and Bioinformatics, Journal of Chemical Information and Modeling, Journal of Computer-Aided Molecular Design, Journal of Medicinal Chemistry and ACS Chemical Biology.

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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