World's Best Scientists 2026 revealed!

D-Index & Metrics

Chemistry

D-Index
65
Citations
13926
World Ranking
7730
National Ranking
263

Didier Rognan publication distribution in Chemistry in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Chemistry in 2026. The highlighted bar marks where Didier Rognan sits on this spectrum.

61–80 publications: 66 scientists 81–100 publications: 302 scientists 101–120 publications: 623 scientists 121–140 publications: 918 scientists 141–160 publications: 1,218 scientists 161–180 publications: 1,350 scientists 181–200 publications: 1,344 scientists 201–220 publications: 1,281 scientists 221–240 publications: 1,216 scientists 241–260 publications: 1,100 scientists 261–280 publications: 979 scientists 281–300 publications: 939 scientists 301–320 publications: 764 scientists 321–340 publications: 643 scientists 341–360 publications: 628 scientists 361–380 publications: 522 scientists 381–400 publications: 459 scientists 401–420 publications: 397 scientists 421–440 publications: 327 scientists 441–460 publications: 270 scientists 461–480 publications: 265 scientists 481–500 publications: 252 scientists 501–520 publications: 201 scientists 521–540 publications: 185 scientists 541–560 publications: 148 scientists 561–580 publications: 148 scientists 581–600 publications: 132 scientists 601–620 publications: 114 scientists 621–640 publications: 104 scientists 641–660 publications: 91 scientists 661–680 publications: 92 scientists 681–700 publications: 73 scientists 701–720 publications: 57 scientists 721–740 publications: 54 scientists 741–760 publications: 67 scientists 761–780 publications: 45 scientists 781–800 publications: 46 scientists 801–820 publications: 39 scientists 821–840 publications: 32 scientists 841–860 publications: 36 scientists 861–880 publications: 29 scientists 881–900 publications: 26 scientists 901–920 publications: 24 scientists 921–940 publications: 14 scientists 941–960 publications: 23 scientists 961–980 publications: 28 scientists 981–1,000 publications: 15 scientists 1,001–1,020 publications: 29 scientists 1,021–1,040 publications: 12 scientists 1,041–1,060 publications: 19 scientists 1,061–1,080 publications: 12 scientists 1,081–1,100 publications: 6 scientists 1,101–1,120 publications: 8 scientists 1,121–1,140 publications: 12 scientists 1,141–1,160 publications: 5 scientists 1,161–1,180 publications: 6 scientists 1,181–1,200 publications: 14 scientists 1,201–1,220 publications: 7 scientists 1,221–1,240 publications: 2 scientists 1,241–1,260 publications: 6 scientists 1,261–1,280 publications: 4 scientists 1,281–1,294 publications: 6 scientists 1,295+ publications: 100 scientists
61 publications 1,295+

This scientist: 194 publications — 30th percentile

30% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 1,295 publications or more.

Didier Rognan D-index placement in Chemistry in 2026

The chart shows the D-index (discipline H-index) distribution of Chemistry scientists ranked by Research.com in 2026. The highlighted bar marks where Didier Rognan sits on this spectrum.

40–41 D-Index: 289 scientists 42–43 D-Index: 612 scientists 44–45 D-Index: 808 scientists 46–47 D-Index: 776 scientists 48–49 D-Index: 835 scientists 50–51 D-Index: 861 scientists 52–53 D-Index: 872 scientists 54–55 D-Index: 933 scientists 56–57 D-Index: 1,051 scientists 58–59 D-Index: 930 scientists 60–61 D-Index: 882 scientists 62–63 D-Index: 834 scientists 64–65 D-Index: 731 scientists 66–67 D-Index: 775 scientists 68–69 D-Index: 683 scientists 70–71 D-Index: 646 scientists 72–73 D-Index: 561 scientists 74–75 D-Index: 501 scientists 76–77 D-Index: 437 scientists 78–79 D-Index: 388 scientists 80–81 D-Index: 354 scientists 82–83 D-Index: 292 scientists 84–85 D-Index: 275 scientists 86–87 D-Index: 254 scientists 88–89 D-Index: 235 scientists 90–91 D-Index: 185 scientists 92–93 D-Index: 192 scientists 94–95 D-Index: 155 scientists 96–97 D-Index: 163 scientists 98–99 D-Index: 125 scientists 100–101 D-Index: 105 scientists 102–103 D-Index: 105 scientists 104–105 D-Index: 112 scientists 106–107 D-Index: 88 scientists 108–109 D-Index: 68 scientists 110–111 D-Index: 69 scientists 112–113 D-Index: 65 scientists 114–115 D-Index: 79 scientists 116–117 D-Index: 61 scientists 118–119 D-Index: 44 scientists 120–121 D-Index: 37 scientists 122–123 D-Index: 40 scientists 124–125 D-Index: 33 scientists 126–127 D-Index: 26 scientists 128–129 D-Index: 34 scientists 130–131 D-Index: 35 scientists 132–133 D-Index: 25 scientists 134–135 D-Index: 27 scientists 136–137 D-Index: 17 scientists 138–139 D-Index: 16 scientists 140–141 D-Index: 20 scientists 142–143 D-Index: 20 scientists 144–145 D-Index: 15 scientists 146–147 D-Index: 9 scientists 148–149 D-Index: 9 scientists 150–151 D-Index: 16 scientists 152–153 D-Index: 11 scientists 154–155 D-Index: 9 scientists 156–157 D-Index: 3 scientists 158 D-Index: 3 scientists 159+ D-Index: 98 scientists
40 D-Index 159+

This scientist: 65 D-Index — 58th percentile

58% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 159 D-Index or more.

Overview

Didier Rognan is affiliated with the University of Strasbourg in France and has contributed extensively to research in biochemistry, genetics, molecular biology, and computer science. Their work spans numerous subfields, including molecular biology, computational theory and mathematics, genetics, pharmacology, and organic chemistry.

The main topics addressed in their research include computational drug discovery methods, protein structure and dynamics, bacterial genetics and biotechnology, microbial natural products and biosynthesis, machine learning in materials science, RNA and protein synthesis mechanisms, and glycosylation and glycoproteins research.

Didier Rognan has published in a variety of scientific venues. Frequent publication venues include:

  • Journal of Chemical Information and Modeling
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Journal of Medicinal Chemistry
  • International Journal of Molecular Sciences
  • Nature Communications

Some recent papers authored or coauthored by Didier Rognan include:

  • "LIT-PCBA: An Unbiased Data Set for Machine Learning and Virtual Screening" (2020), Journal of Chemical Information and Modeling
  • "On the Frustration to Predict Binding Affinities from Protein-Ligand Structures with Deep Neural Networks" (2022), Journal of Medicinal Chemistry
  • "True Accuracy of Fast Scoring Functions to Predict High-Throughput Screening Data from Docking Poses: The Simpler the Better" (2021), Journal of Chemical Information and Modeling
  • "Estimating the Similarity between Protein Pockets" (2022), International Journal of Molecular Sciences
  • "A Computer Vision Approach to Align and Compare Protein Cavities: Application to Fragment-Based Drug Design" (2020), Journal of Medicinal Chemistry

Their frequent coauthors include:

  • Merveille Eguida
  • François Sindt
  • Guillaume Bret
  • Pascal Villa
  • Sakonwan Kuhaudomlarp

Didier Rognan's research activity reflects an integration of computational approaches and experimental insights across chemistry, biology, and computer science, with particular attention to drug discovery techniques, structural biology of proteins, and the application of machine learning models in molecular sciences.

Best Publications

  • Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations.

    Caterina Bissantz;Gerd Folkers;Didier Rognan

  • Comparative evaluation of eight docking tools for docking and virtual screening accuracy

    Esther Kellenberger;Jordi Rodrigo;Pascal Muller;Didier Rognan

  • Optimizing fragment and scaffold docking by use of molecular interaction fingerprints.

    Gilles Marcou;Didier Rognan

  • Identification of a low–molecular weight TrkB antagonist with anxiolytic and antidepressant activity in mice

    Maxime Cazorla;Joël Prémont;Andre Mann;Nicolas Girard

  • Protein-based virtual screening of chemical databases. II. Are homology models of G-Protein Coupled Receptors suitable targets?

    Caterina Bissantz;Philippe Bernard;Marcel Hibert;Didier Rognan

  • Probing the Cysteine-34 Position of Endogenous Serum Albumin with Thiol-Binding Doxorubicin Derivatives. Improved Efficacy of an Acid-Sensitive Doxorubicin Derivative with Specific Albumin-Binding Properties Compared to That of the Parent Compound

    Felix Kratz;André Warnecke;Karin Scheuermann;Cornelia Stockmar

  • Chemogenomic approaches to rational drug design.

    Didier Rognan

  • sc-PDB: a 3D-database of ligandable binding sites—10 years on

    Jérémy Desaphy;Guillaume Bret;Didier Rognan;Esther Kellenberger

  • A chemogenomic analysis of the transmembrane binding cavity of human G-protein-coupled receptors.

    Jean-Sebastien Surgand;Jordi Rodrigo;Esther Kellenberger;Didier Rognan

  • Predicting binding affinities of protein ligands from three-dimensional models: application to peptide binding to class I major histocompatibility proteins.

    Didier Rognan;Sanne Lise Lauemøller;Arne Holm;Søren Buus

  • Positive and negative allosteric modulators of the Ca2+-sensing receptor interact within overlapping but not identical binding sites in the transmembrane domain.

    Christophe Petrel;Albane Kessler;Philippe Dauban;Robert H. Dodd

  • sc-PDB: an annotated database of druggable binding sites from the Protein Data Bank.

    Esther Kellenberger;Pascal Muller;Claire Schalon;Guillaume Bret

  • N,N'-linked oligoureas as foldamers: chain length requirements for helix formation in protic solvent investigated by circular dichroism, NMR spectroscopy, and molecular dynamics.

    Aude Violette;Marie Christine Averlant-Petit;Vincent Semetey;Christine Hemmerlin

  • LIT-PCBA: An Unbiased Data Set for Machine Learning and Virtual Screening.

    Viet-Khoa Tran-Nguyen;Célien Jacquemard;Didier Rognan

  • Encoding protein-ligand interaction patterns in fingerprints and graphs.

    Jérémy Desaphy;Eric Raimbaud;Pierre Ducrot;Didier Rognan

  • On the Frustration to Predict Binding Affinities from Protein–Ligand Structures with Deep Neural Networks

    Unknown

  • Comparison and Druggability Prediction of Protein–Ligand Binding Sites from Pharmacophore-Annotated Cavity Shapes

    Jérémy Desaphy;Karima Azdimousa;Esther Kellenberger;Didier Rognan

  • Structure-Based Approaches to Target Fishing and Ligand Profiling

    Didier Rognan

  • Beware of Machine Learning-Based Scoring Functions—On the Danger of Developing Black Boxes

    Joffrey Gabel;Jérémy Desaphy;Didier Rognan

  • Selective structure-based virtual screening for full and partial agonists of the beta2 adrenergic receptor.

    Chris de Graaf;Didier Rognan

  • Delineating a Ca2+ binding pocket within the venus flytrap module of the human calcium-sensing receptor.

    Caroline Silve;Christophe Petrel;Christine Leroy;Henri Bruel

  • Estrogen receptor alpha as a key target of red wine polyphenols action on the endothelium.

    Matthieu Chalopin;Angela Tesse;Maria Carmen Martínez;Didier Rognan

Frequent Co-Authors

Gerd Folkers
Gerd Folkers ETH Zurich
Martial Ruat
Martial Ruat Centre national de la recherche scientifique, CNRS
Chris de Graaf
Chris de Graaf Vrije Universiteit Amsterdam
Pierre Sokoloff
Pierre Sokoloff Grenoble Alpes University
Bruno Giros
Bruno Giros McGill University
Gilles Guichard
Gilles Guichard Centre national de la recherche scientifique, CNRS
Isabelle J. Schalk
Isabelle J. Schalk University of Strasbourg
Robert H. Dodd
Robert H. Dodd Centre national de la recherche scientifique, CNRS
Philippe Dauban
Philippe Dauban Institut de Chimie des Substances Naturelles
Jean-Paul Briand
Jean-Paul Briand Centre national de la recherche scientifique, CNRS

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