David Simoncini

612 total citations
19 papers, 337 citations indexed

About

David Simoncini is a scholar working on Molecular Biology, Materials Chemistry and Computational Theory and Mathematics. According to data from OpenAlex, David Simoncini has authored 19 papers receiving a total of 337 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 11 papers in Materials Chemistry and 4 papers in Computational Theory and Mathematics. Recurrent topics in David Simoncini's work include Protein Structure and Dynamics (12 papers), Enzyme Structure and Function (11 papers) and RNA and protein synthesis mechanisms (6 papers). David Simoncini is often cited by papers focused on Protein Structure and Dynamics (12 papers), Enzyme Structure and Function (11 papers) and RNA and protein synthesis mechanisms (6 papers). David Simoncini collaborates with scholars based in France, Japan and Belgium. David Simoncini's co-authors include Kam Y. J. Zhang, Thomas Schiex, Jeremy R. H. Tame, Arnout Voet, Hiroki Noguchi, Christine Addy, Sophie Barbe, Francois Berenger, Satoru Unzai and Daiki Terada and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Blood.

In The Last Decade

David Simoncini

19 papers receiving 337 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David Simoncini France 11 274 135 40 23 22 19 337
Paweł Dąbrowski-Tumański Poland 13 488 1.8× 132 1.0× 42 1.1× 48 2.1× 24 1.1× 23 617
Arthur G. Street United States 6 435 1.6× 193 1.4× 23 0.6× 21 0.9× 34 1.5× 7 506
Lauren L. Porter United States 16 633 2.3× 265 2.0× 45 1.1× 9 0.4× 14 0.6× 28 742
Amanda L. Jonsson United States 10 448 1.6× 187 1.4× 24 0.6× 20 0.9× 29 1.3× 15 504
Wanda Niemyska Poland 11 358 1.3× 103 0.8× 64 1.6× 17 0.7× 13 0.6× 23 509
Ofer Rahat Israel 5 340 1.2× 110 0.8× 70 1.8× 18 0.8× 51 2.3× 7 409
R. Gonzalo Parra Argentina 13 568 2.1× 144 1.1× 56 1.4× 9 0.4× 14 0.6× 24 643
Daisuke Kiga Japan 13 859 3.1× 31 0.2× 51 1.3× 34 1.5× 31 1.4× 40 928
Elizabeth M. Meiering Canada 8 336 1.2× 123 0.9× 20 0.5× 17 0.7× 33 1.5× 11 392
Aron Broom Canada 12 466 1.7× 167 1.2× 20 0.5× 24 1.0× 36 1.6× 18 546

Countries citing papers authored by David Simoncini

Since Specialization
Citations

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

Fields of papers citing papers by David Simoncini

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Simoncini

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

All Works

19 of 19 papers shown
1.
Adolf‐Bryfogle, Jared, James W. Bowman, Sebástien Vérel, et al.. (2024). Complete combinatorial mutational enumeration of a protein functional site enables sequence‐landscape mapping and identifies highly‐mutated variants that retain activity. Protein Science. 33(8). e5109–e5109. 1 indexed citations
2.
Alliot, Jean‐Marc, Pierre‐Yves Dumas, François Vergez, et al.. (2024). Artificial intelligence-based prediction models for acute myeloid leukemia using real-life data: A DATAML registry study. Leukemia Research. 136. 107437–107437. 10 indexed citations
3.
Simoncini, David, François Vergez, Pierre‐Yves Dumas, et al.. (2021). Artificial Intelligence-Based Predictive Models for Acute Myeloid Leukemia. Blood. 138(Supplement 1). 3389–3389. 2 indexed citations
4.
Padhi, Aditya K., Sophie Barbe, Thomas Schiex, et al.. (2021). Seven Amino Acid Types Suffice to Create the Core Fold of RNA Polymerase. Journal of the American Chemical Society. 143(39). 15998–16006. 20 indexed citations
5.
Simoncini, David, et al.. (2019). Positive multistate protein design. Bioinformatics. 36(1). 122–130. 13 indexed citations
6.
Noguchi, Hiroki, Christine Addy, David Simoncini, et al.. (2018). Computational design of symmetrical eight-bladed β-propeller proteins. IUCrJ. 6(1). 46–55. 27 indexed citations
7.
Simoncini, David, Kam Y. J. Zhang, Thomas Schiex, & Sophie Barbe. (2018). A structural homology approach for computational protein design with flexible backbone. Bioinformatics. 35(14). 2418–2426. 4 indexed citations
8.
Simoncini, David, Sophie Barbe, Thomas Schiex, & Sebástien Vérel. (2018). Fitness landscape analysis around the optimum in computational protein design. Proceedings of the Genetic and Evolutionary Computation Conference. 355–362. 10 indexed citations
9.
Simoncini, David, Thomas Schiex, & Kam Y. J. Zhang. (2017). Balancing exploration and exploitation in population-based sampling improves fragment-basedde novoprotein structure prediction. Proteins Structure Function and Bioinformatics. 85(5). 852–858. 12 indexed citations
10.
Voet, Arnout, David Simoncini, Jeremy R. H. Tame, & Kam Y. J. Zhang. (2016). Evolution-Inspired Computational Design of Symmetric Proteins. Methods in molecular biology. 1529. 309–322. 10 indexed citations
11.
Simoncini, David, Hiroya Nakata, Koji Ogata, Shinichiro Nakamura, & Kam Y. J. Zhang. (2015). Quality Assessment of Predicted Protein Models Using Energies Calculated by the Fragment Molecular Orbital Method. Molecular Informatics. 34(2-3). 97–104. 12 indexed citations
12.
Simoncini, David, et al.. (2015). Guaranteed Discrete Energy Optimization on Large Protein Design Problems. Journal of Chemical Theory and Computation. 11(12). 5980–5989. 34 indexed citations
13.
Voet, Arnout, Hiroki Noguchi, Christine Addy, et al.. (2014). Computational design of a self-assembling symmetrical β-propeller protein. Proceedings of the National Academy of Sciences. 111(42). 15102–15107. 99 indexed citations
14.
Simoncini, David & Kam Y. J. Zhang. (2013). Efficient Sampling in Fragment-Based Protein Structure Prediction Using an Estimation of Distribution Algorithm. PLoS ONE. 8(7). e68954–e68954. 21 indexed citations
15.
Simoncini, David, et al.. (2012). A Probabilistic Fragment-Based Protein Structure Prediction Algorithm. PLoS ONE. 7(7). e38799–e38799. 34 indexed citations
16.
Simoncini, David, et al.. (2012). Error-estimation-guided rebuilding ofde novomodels increases the success rate ofab initiophasing. Acta Crystallographica Section D Biological Crystallography. 68(11). 1522–1534. 4 indexed citations
17.
Simoncini, David, et al.. (2012). Correction: A Probabilistic Fragment-Based Protein Structure Prediction Algorithm. PLoS ONE. 7(10). 2 indexed citations
18.
Berenger, Francois, et al.. (2011). Durandal: Fast exact clustering of protein decoys. Journal of Computational Chemistry. 33(4). 471–474. 16 indexed citations
19.
Simoncini, David, Sebástien Vérel, Philippe Collard, & Manuel Clergue. (2011). Centric selection: a way to tune the exploration/exploitation trade-off. arXiv (Cornell University). 6 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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