Sergio Decherchi

3.6k citations
86 papers · 1.7k · h-index 22

Impact in

Papers in

Sergio Decherchi

79 papers receiving 1.7k citations

Peers

Sergio Decherchi
Comparison fields: 5 of 144
  • Computational Theory and Mathematics 483
  • Molecular Biology 868
  • Computer Vision and Pattern Recognition 158
  • Artificial Intelligence 245
  • Health Informatics 8
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Lee Sael South Korea
Marc Berndl United States
Kevin McCloskey United States
David Duvenaud Canada
Payel Das United States
Shawn Martin United States
Steven Kearnes United States
Dariusz Plewczyński Poland
Timothy Hirzel United States
Sean B. Holden United Kingdom
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Citations per field
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Citations per year

Countries citing papers authored by Sergio Decherchi

Since Specialization
Citations

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

Fields of papers citing papers by Sergio Decherchi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 86 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2020209
2 2015123
3 2013102
4 201595
5 201184
6 201680
7 201275
8 201260
9 201759
10 201557
11 201453
12 201851
13 201346
14 202145
15 201941
16 201939
17 202135
18 200934
19 201834
20 201828

About Sergio Decherchi

Sergio Decherchi is a scholar working on Molecular Biology, Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Theory and Mathematics and Materials Chemistry, having authored 86 papers that have together received 1.7k indexed citations. Recurring topics across this work include Protein Structure and Dynamics (21 papers), Computational Drug Discovery Methods (13 papers), Face and Expression Recognition (10 papers), Neural Networks and Applications (10 papers), Machine Learning and ELM (9 papers), Machine Learning in Materials Science (7 papers), Data Visualization and Analytics (6 papers) and Receptor Mechanisms and Signaling (6 papers). The work is most often cited by research in Computational Theory and Mathematics (483 citations), Molecular Biology (868 citations), Computer Vision and Pattern Recognition (158 citations), Artificial Intelligence (245 citations) and Health Informatics (8 citations). Sergio Decherchi has collaborated with scholars based in Italy, Switzerland and United States. Frequent co-authors include Andrea Cavalli, Walter Rocchia, Rodolfo Zunino, Paolo Gastaldo, Giovanni Bottegoni, Luca Mollica, Andrea Spitaleri, Roberto Gaspari, Judith Redi and Anna Berteotti. Their work appears in journals such as Journal of Chemical Theory and Computation, Journal of Chemical Information and Modeling, Neurocomputing, Bioinformatics and The Journal of Physical Chemistry Letters.

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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