Gerhard Heßler

55 papers receiving 2.0k citations

Peers

Gerhard Heßler
Comparison fields: 5 of 141
  • Computational Theory and Mathematics 634
  • Health Informatics 29
  • Molecular Biology 1.5k
  • Organic Chemistry 452
  • Sensory Systems 66
Replace S. Barret Kalindjian with:
S. Barret Kalindjian United Kingdom
Weiqiang Lü China
Anneli Karlsson Sweden
Alexander Heifetz United Kingdom
Oleg Ursu United States
Rita Santos United Kingdom
Jiankun Lyu United States
Ottavia Spiga Italy
Trent E. Balius United States
John W. Davies Switzerland
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Citations per field
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S. Barret Kalindjian · 1×
Citations per year

Countries citing papers authored by Gerhard Heßler

Since Specialization
Citations

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

Fields of papers citing papers by Gerhard Heßler

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 56 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2002391
2 2018243
3 2021168
4 1996166
5 2005127
6 201598
7 199683
8 201054
9 199650
10 199841
11 202241
12 200039
13 201035
14 202134
15 202032
16 201230
17 201430
18 201230
19 201229
20 202027

About Gerhard Heßler

Gerhard Heßler is a scholar working on Molecular Biology, Computational Theory and Mathematics, Materials Chemistry, Organic Chemistry and Spectroscopy, having authored 56 papers that have together received 2.1k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (29 papers), Chemical Synthesis and Analysis (12 papers), Machine Learning in Materials Science (10 papers), Protein Structure and Dynamics (8 papers), Receptor Mechanisms and Signaling (6 papers), Chemistry and Chemical Engineering (4 papers), Monoclonal and Polyclonal Antibodies Research (4 papers) and Analytical Chemistry and Chromatography (4 papers). The work is most often cited by research in Computational Theory and Mathematics (634 citations), Health Informatics (29 citations), Molecular Biology (1.5k citations), Organic Chemistry (452 citations) and Sensory Systems (66 citations). Gerhard Heßler has collaborated with scholars based in Germany, France and United States. Frequent co-authors include Thomas Klabunde, Karl‐Heinz Baringhaus, Hans Matter, Horst Kessler, Matthias Hoffmann, Andreas Evers, Gerhard Müller, Erich Graf von Roedern, Elisabeth Lohof and Christoph Grebner. Their work appears in journals such as Journal of Chemical Information and Modeling, Journal of Medicinal Chemistry, ChemMedChem, Journal of Cheminformatics and Angewandte Chemie International Edition.

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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