World's Best Scientists 2026 revealed!
David A. Winkler

David A. Winkler

D-Index & Metrics

Engineering and Technology

D-Index
57
Citations
12651
World Ranking
2631
National Ranking
135

David A. Winkler 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 David A. Winkler 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: 221 publications — 55th percentile

55% of scientists in this discipline score the same or lower.

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

David A. Winkler 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 David A. Winkler 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: 57 D-Index — 74th percentile

74% of scientists in this discipline score the same or lower.

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

Overview

David A. Winkler is affiliated with La Trobe University in Australia and conducts research primarily in the fields of Materials Science and Engineering. Their work encompasses various subfields such as Materials Chemistry, Molecular Biology, Biomedical Engineering, Computational Theory and Mathematics, and Artificial Intelligence.

The main research topics covered by Winkler include:

  • Machine Learning in Materials Science
  • Computational Drug Discovery Methods
  • 3D Printing in Biomedical Research
  • SARS-CoV-2 and COVID-19 Research
  • Nanoparticles: synthesis and applications
  • Ion-surface interactions and analysis
  • Advanced Photocatalysis Techniques

Winkler has coauthored multiple papers with several collaborators, with frequent co-authors including:

  • Grazziela P. Figueredo
  • Morgan R. Alexander
  • Wil Gardner
  • Benjamin W. Muir
  • Paul J. Pigram

The most common venues where Winkler has published include:

  • Analytical Chemistry
  • Advanced Materials Interfaces
  • Chemical Society Reviews
  • OPAL (Open@LaTrobe) (La Trobe University)
  • bioRxiv (Cold Spring Harbor Laboratory)

Selected recent papers authored or co-authored by Winkler are:

  • QSAR without borders, 2020, Chemical Society Reviews
  • Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery, 2022, Chemical Reviews
  • Systematic Comparison of the Structural and Dynamic Properties of Commonly Used Water Models for Molecular Dynamics Simulations, 2021, Journal of Chemical Information and Modeling
  • A critical overview of computational approaches employed for COVID-19 drug discovery, 2021, Chemical Society Reviews
  • Immune Modulation by Design: Using Topography to Control Human Monocyte Attachment and Macrophage Differentiation, 2020, Advanced Science

In addition to journal publications, Winkler has contributed to book literature. Notably, they published a book titled Ethics of Medical Innovation, Experimentation, and Enhancement in Military and Humanitarian Contexts in 2020 through Springer International Publishing.

Best Publications

  • QSAR without borders

    Eugene N. Muratov;Eugene N. Muratov;Jürgen Bajorath;Robert P. Sheridan;Igor V. Tetko

  • Beware of R2: Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models

    David L J Alexander;Alexander Tropsha;David Alan Winkler

  • Bayesian regularization of neural networks.

    Frank Burden;David Winkler

  • Quantitative Structure–Property Relationship Modeling of Diverse Materials Properties

    Tu Le;V. Chandana Epa;Frank R. Burden;David A. Winkler

  • Robust QSAR models using Bayesian regularized neural networks.

    Frank R. Burden;David A. Winkler

  • Opening the black box of neural networks: methods for interpreting neural network models in clinical applications

    Zhongheng Zhang;Marcus W Beck;David A Winkler;Bin Huang

  • A renaissance of neural networks in drug discovery

    Igor I Baskin;David Winkler;Igor V Tetko

  • Materials Genome in Action: Identifying the Performance Limits of Physical Hydrogen Storage

    Aaron William Thornton;Cory M. Simon;Jihan Kim;Ohmin Kwon

  • Systematic Comparison of the Structural and Dynamic Properties of Commonly Used Water Models for Molecular Dynamics Simulations.

    Sachini P. Kadaoluwa Pathirannahalage;Sachini P. Kadaoluwa Pathirannahalage;Nastaran Meftahi;Aaron Elbourne;Alessia C. G. Weiss

  • Modeling biological activities of nanoparticles.

    Chandana Epa;Frank Robert Burden;Carlos Tassa;Ralph Weissleder

  • Discovery and Optimization of Materials Using Evolutionary Approaches.

    Tu Le;David Alan Winkler

  • Computational Modeling and Simulation of CO2 Capture by Aqueous Amines

    Xin Yang;Xin Yang;Robert J. Rees;William Conway;Graeme Puxty

  • Applying quantitative structure–activity relationship approaches to nanotoxicology: Current status and future potential

    David A. Winkler;Enrico Mombelli;Antonio Pietroiusti;Lang Tran

  • The role of quantitative structure - activity relationships (QSAR) in biomolecular discovery

    David A. Winkler

  • A critical overview of computational approaches employed for COVID-19 drug discovery.

    Eugene N Muratov;Rommie Amaro;Carolina H Andrade;Nathan Brown

  • Understanding the Roles of the "Two QSARs".

    Toshio Fujita;David Alan Winkler

  • Towards chromate-free corrosion inhibitors: structure–property models for organic alternatives

    David Alan Winkler;Michael Breedon;Anthony E Hughes;Frank Robert Burden

  • Use of automatic relevance determination in QSAR studies using Bayesian neural networks.

    Frank R. Burden;Martyn G. Ford;David C. Whitley;David A. Winkler

  • Materials for stem cell factories of the future

    Adam Celiz;James G W Smith;Robert Samuel M Langer;Daniel G Anderson

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

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

  • Comparison of linear and nonlinear classification algorithms for the prediction of drug and chemical metabolism by human UDP-glucuronosyltransferase isoforms.

    Michael J. Sorich;John O. Miners;Ross A. McKinnon;David A. Winkler

  • Classification of emergence and its relation to self-organization

    Julianne. D. Halley;David A. Winkler

Frequent Co-Authors

Feng Wang
Feng Wang Swinburne University of Technology
Morgan R. Alexander
Morgan R. Alexander University of Nottingham
David N. Haylock
David N. Haylock Commonwealth Scientific and Industrial Research Organisation
Susan K. Nilsson
Susan K. Nilsson Commonwealth Scientific and Industrial Research Organisation
Olexandr Isayev
Olexandr Isayev Carnegie Mellon University
Joseph G. Shapter
Joseph G. Shapter University of Queensland
George W. J. Fleet
George W. J. Fleet University of Oxford
Martyn C. Davies
Martyn C. Davies University of Nottingham

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