World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
38
Citations
5865
World Ranking
8052
National Ranking
507

Valerie J. Gillet publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Valerie J. Gillet sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 119 publications — 15th percentile

15% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Valerie J. Gillet D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Valerie J. Gillet sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 38 D-Index — 20th percentile

20% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Valerie J. Gillet is affiliated with the University of Sheffield in the United Kingdom. Their research spans multiple fields, primarily focusing on Medicine and Computer Science. Within these broad areas, they have contributed notably to subfields such as Computational Theory and Mathematics, Materials Chemistry, Oncology, Experimental and Cognitive Psychology, and Physiology.

The main topics of their scientific work revolve around Computational Drug Discovery Methods and Machine Learning in Materials Science. Additional research interests include Cancer survivorship and care, Sleep and related disorders, Cancer-related cognitive impairment studies, Chemical Synthesis and Analysis, and Childhood Cancer Survivors' Quality of Life.

Gillet has authored several papers published in recognized scientific journals. Some recent publications include:

  • Enhancing reaction-based de novo design using a multi-label reaction class recommender, 2020, Journal of Computer-Aided Molecular Design
  • Analysis of the benefits of imputation models over traditional QSAR models for toxicity prediction, 2022, Journal of Cheminformatics
  • RENATE: A Pseudo-retrosynthetic Tool for Synthetically Accessible de novo Design, 2021, Molecular Informatics
  • Interpreting Neural Network Models for Toxicity Prediction by Extracting Learned Chemical Features, 2024, Journal of Chemical Information and Modeling
  • A multidisciplinary weight loss intervention in obese adolescents with and without sleep-disordered breathing improves cardiometabolic health, whether SDB was normalized or not, 2020, Sleep Medicine

Their frequent coauthors highlight collaborative research efforts. The most frequent collaborators include Fabienne Mougin, Chloé Drozd, Nathalie Méneveau, Elsa Curtit, and Quentin Jacquinot.

Valerie J. Gillet's work is regularly published in several scientific journals, with multiple publications appearing in venues such as Médecine du Sommeil, Journal of Cheminformatics, Molecular Informatics, Research Square, and Journal of Chemical Information and Modeling.

Best Publications

  • SPROUT: A program for structure generation

    Valerie J. Gillet;A. Peter Johnson;Paulina Mata;Sandor Sike

  • Identification of biological activity profiles using substructural analysis and genetic algorithms.

    Valerie J. Gillet;Peter Willett;John Bradshaw

  • SPROUT: recent developments in the de novo design of molecules.

    V J Gillet;W Newell;P Mata;G Myatt

  • Combinatorial library design using a multiobjective genetic algorithm.

    Valerie J. Gillet;Wael Khatib;Peter Willett;Peter J. Fleming

  • The Effectiveness of Reactant Pools for Generating Structurally-Diverse Combinatorial Libraries

    Valerie J. Gillet;Peter Willett;John Bradshaw

  • Similarity searching using reduced graphs.

    Valerie J. Gillet;Peter Willett;John Bradshaw

  • Review of ring perception algorithms for chemical graphs

    Geoffrey M. Downs;Valerie J. Gillet;John D. Holliday;Michael F. Lynch

  • A comparison of the pharmacophore identification programs: Catalyst, DISCO and GASP.

    Yogendra Patel;Valerie J. Gillet;Gianpaolo Bravi;Andrew R. Leach

  • Selecting Combinatorial Libraries to Optimize Diversity and Physical Properties

    Valerie J. Gillet;Peter Willett;John Bradshaw;Darren V. S. Green

  • Glossary of terms used in computational drug design, part II (IUPAC Recommendations 2015)

    Yvonne C. Martin;Ruben Abagyan;György G. Ferenczy;Val J. Gillet

  • Lead optimization using matched molecular pairs: inclusion of contextual information for enhanced prediction of HERG inhibition, solubility, and lipophilicity.

    George Papadatos;Muhammad Alkarouri;Valerie J Gillet;Peter Willett

  • Enhancing the effectiveness of virtual screening by fusing nearest neighbor lists: a comparison of similarity coefficients.

    Martin Whittle;Valerie J. Gillet;Peter Willett;Alexander Alex

  • Multiobjective Optimization in Quantitative Structure−Activity Relationships: Deriving Accurate and Interpretable QSARs

    Orazio Nicolotti;Valerie J. Gillet;Peter J. Fleming;Darren V. S. Green

  • Scaffold hopping using clique detection applied to reduced graphs.

    Edward J. Barker;David Buttar;David A. Cosgrove;Eleanor J. Gardiner

  • Similarity searching in files of three-dimensional chemical structures: analysis of the BIOSTER database using two-dimensional fingerprints and molecular field descriptors

    Ansgar Schuffenhauer;Valerie J. Gillet;Peter Willett

  • Comparison of conformational analysis techniques to generate pharmacophore hypotheses using catalyst.

    Rajendra Kristam;Valerie J. Gillet;Richard A. Lewis;David A. Thorner

  • Designing focused libraries using MoSELECT.

    Valerie J. Gillet;Peter Willett;Peter J. Fleming;Darren V.S. Green

  • Analysis of data fusion methods in virtual screening: similarity and group fusion.

    Martin Whittle;Valerie J. Gillet;Peter Willett;Jens Loesel

  • Further development of reduced graphs for identifying bioactive compounds.

    Edward J. Barker;Eleanor J. Gardiner;Valerie J. Gillet;Paula Kitts

  • Generation of multiple pharmacophore hypotheses using multiobjective optimisation techniques.

    Simon J. Cottrell;Valerie J. Gillet;Robin Taylor;David J. Wilton

  • Computer storage and retrieval of generic chemical structures in patents. 8. Reduced chemical graphs and their applications in generic chemical structure retrieval

    Valerie J. Gillet;Geoffrey M. Downs;Al Ling;Michael F. Lynch

  • SPROUT: 3D Structure Generation Using Templates

    Paulina Mata;Valerie J. Gillet;A. Peter Johnson;Jorge Lampreia

Frequent Co-Authors

Peter Willett
Peter Willett University of Sheffield
Peter J. Fleming
Peter J. Fleming University of Sheffield
Nigel Ford
Nigel Ford University of Sheffield
Visakan Kadirkamanathan
Visakan Kadirkamanathan University of Sheffield
Maciej Haranczyk
Maciej Haranczyk Madrid Institute for Advanced Studies
Ruben Abagyan
Ruben Abagyan University of California, San Diego
David A. Winkler
David A. Winkler La Trobe University
Jim A. Thomas
Jim A. Thomas University of Sheffield

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