Didier Devaurs

1.3k total citations
29 papers, 748 citations indexed

About

Didier Devaurs is a scholar working on Molecular Biology, Computer Vision and Pattern Recognition and Computational Theory and Mathematics. According to data from OpenAlex, Didier Devaurs has authored 29 papers receiving a total of 748 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Molecular Biology, 5 papers in Computer Vision and Pattern Recognition and 5 papers in Computational Theory and Mathematics. Recurrent topics in Didier Devaurs's work include Protein Structure and Dynamics (13 papers), Computational Drug Discovery Methods (5 papers) and vaccines and immunoinformatics approaches (5 papers). Didier Devaurs is often cited by papers focused on Protein Structure and Dynamics (13 papers), Computational Drug Discovery Methods (5 papers) and vaccines and immunoinformatics approaches (5 papers). Didier Devaurs collaborates with scholars based in United States, France and Austria. Didier Devaurs's co-authors include Dinler A. Antunes, Lydia E. Kavraki, Thierry Siméon, Juan Cortés, Mark Moll, Gregory Lizée, Robin Gras, Maurício Rigo, Stefanie Lindstaedt and Marc Vaisset and has published in prestigious journals such as Nucleic Acids Research, Cancer Research and Scientific Reports.

In The Last Decade

Didier Devaurs

29 papers receiving 720 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Didier Devaurs United States 15 392 156 140 96 92 29 748
Zhiqiang Ma China 20 960 2.4× 90 0.6× 143 1.0× 67 0.7× 12 0.1× 64 1.3k
Xiaolei Zhu China 23 1.1k 2.9× 36 0.2× 283 2.0× 26 0.3× 53 0.6× 64 1.6k
Bo Liao China 29 1.6k 4.0× 79 0.5× 216 1.5× 30 0.3× 22 0.2× 111 2.1k
Shiqiang Zhang China 13 391 1.0× 57 0.4× 36 0.3× 30 0.3× 22 0.2× 53 1.4k
Tun‐Wen Pai Taiwan 19 446 1.1× 213 1.4× 23 0.2× 84 0.9× 14 0.2× 116 1.1k
V. Srinivasa Rao India 9 406 1.0× 13 0.1× 86 0.6× 37 0.4× 22 0.2× 32 768
Qiao Liu China 23 1.1k 2.8× 82 0.5× 219 1.6× 137 1.4× 6 0.1× 117 1.9k
Hao Lv China 30 2.0k 5.1× 70 0.4× 198 1.4× 44 0.5× 21 0.2× 65 2.3k
Eric Paquet Canada 12 183 0.5× 66 0.4× 88 0.6× 15 0.2× 15 0.2× 59 697

Countries citing papers authored by Didier Devaurs

Since Specialization
Citations

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

Fields of papers citing papers by Didier Devaurs

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Didier Devaurs

This figure shows the co-authorship network connecting the top 25 collaborators of Didier Devaurs. A scholar is included among the top collaborators of Didier Devaurs 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 Didier Devaurs. Didier Devaurs is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Rigo, Maurício, Didier Devaurs, Cecilia Clementi, et al.. (2023). EnGens: a computational framework for generation and analysis of representative protein conformational ensembles. Briefings in Bioinformatics. 24(4). 14 indexed citations
2.
Devaurs, Didier, et al.. (2022). 3pHLA-score improves structure-based peptide-HLA binding affinity prediction. Scientific Reports. 12(1). 10749–10749. 9 indexed citations
3.
Devaurs, Didier, et al.. (2021). DINC-COVID: A webserver for ensemble docking with flexible SARS-CoV-2 proteins. Computers in Biology and Medicine. 139. 104943–104943. 7 indexed citations
4.
Devaurs, Didier, Dinler A. Antunes, & Lydia E. Kavraki. (2020). Computational analysis of complement inhibitor compstatin using molecular dynamics. Journal of Molecular Modeling. 26(9). 231–231. 6 indexed citations
5.
Devaurs, Didier, et al.. (2019). Using parallelized incremental meta-docking can solve the conformational sampling issue when docking large ligands to proteins. BMC Molecular and Cell Biology. 20(1). 42–42. 34 indexed citations
6.
Antunes, Dinler A., et al.. (2019). Structure-based Methods for Binding Mode and Binding Affinity Prediction for Peptide-MHC Complexes. Current Topics in Medicinal Chemistry. 18(26). 2239–2255. 46 indexed citations
7.
Devaurs, Didier, Malvina Papanastasiou, Dinler A. Antunes, et al.. (2018). Native state of complement protein C3d analysed via hydrogen exchange and conformational sampling. International Journal of Computational Biology and Drug Design. 11(1/2). 90–90. 6 indexed citations
8.
Antunes, Dinler A., Didier Devaurs, Mark Moll, Gregory Lizée, & Lydia E. Kavraki. (2018). General Prediction of Peptide-MHC Binding Modes Using Incremental Docking: A Proof of Concept. Scientific Reports. 8(1). 4327–4327. 39 indexed citations
9.
Devaurs, Didier, Dinler A. Antunes, & Lydia E. Kavraki. (2018). Revealing Unknown Protein Structures Using Computational Conformational Sampling Guided by Experimental Hydrogen-Exchange Data. International Journal of Molecular Sciences. 19(11). 3406–3406. 2 indexed citations
10.
Antunes, Dinler A., et al.. (2017). DINC 2.0: A New Protein–Peptide Docking Webserver Using an Incremental Approach. Cancer Research. 77(21). e55–e57. 93 indexed citations
11.
Devaurs, Didier, Dinler A. Antunes, Malvina Papanastasiou, et al.. (2017). Coarse-Grained Conformational Sampling of Protein Structure Improves the Fit to Experimental Hydrogen-Exchange Data. Frontiers in Molecular Biosciences. 4. 13–13. 17 indexed citations
12.
Devaurs, Didier, et al.. (2016). Defining Low-Dimensional Projections to Guide Protein Conformational Sampling. Journal of Computational Biology. 24(1). 79–89. 5 indexed citations
13.
Antunes, Dinler A., Didier Devaurs, & Lydia E. Kavraki. (2015). Understanding the challenges of protein flexibility in drug design. Expert Opinion on Drug Discovery. 10(12). 1301–1313. 91 indexed citations
14.
Devaurs, Didier, Thierry Siméon, & Juan Cortés. (2015). Optimal Path Planning in Complex Cost Spaces With Sampling-Based Algorithms. IEEE Transactions on Automation Science and Engineering. 13(2). 415–424. 105 indexed citations
15.
Devaurs, Didier, Amarda Shehu, Thierry Siméon, & Juan Cortés. (2014). Sampling-based methods for a full characterization of energy landscapes of small peptides. 37–44. 3 indexed citations
16.
Devaurs, Didier, et al.. (2013). MoMA-LigPath: a web server to simulate protein–ligand unbinding. Nucleic Acids Research. 41(W1). W297–W302. 34 indexed citations
17.
Devaurs, Didier, Thierry Siméon, & Juan Cortés. (2013). Parallelizing RRT on Large-Scale Distributed-Memory Architectures. IEEE Transactions on Robotics. 29(2). 571–579. 20 indexed citations
18.
Devaurs, Didier, et al.. (2012). EXPLOITING THE USER INTERACTION CONTEXT FOR AUTOMATIC TASK DETECTION. Applied Artificial Intelligence. 26(1-2). 58–80. 5 indexed citations
19.
Devaurs, Didier & Robin Gras. (2009). Species abundance patterns in an ecosystem simulation studied through Fisher’s logseries. Simulation Modelling Practice and Theory. 18(1). 100–123. 17 indexed citations
20.
Devaurs, Didier, et al.. (2009). UICO. 1–10. 29 indexed citations

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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