World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
36
Citations
4555
World Ranking
8779
National Ranking
208

Dragos Horvath 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 Dragos Horvath 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: 133 publications — 20th percentile

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

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

Dragos Horvath 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 Dragos Horvath 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: 36 D-Index — 13th percentile

13% of scientists in this discipline score the same or lower.

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

Overview

Dragos Horvath is affiliated with the University of Strasbourg in France. Their research spans several scientific domains with a focus on the intersection of biochemistry, molecular biology, and computer science.

The main fields of study represented in their work include:

  • Biochemistry, Genetics and Molecular Biology
  • Computer Science

Within these broader fields, they have explored various subfields such as:

  • Molecular Biology
  • Computational Theory and Mathematics
  • Pharmacology
  • Biomedical Engineering
  • Materials Chemistry

Dragos Horvath's research topics cover a diverse range of areas, including:

  • Computational Drug Discovery Methods
  • Microbial Natural Products and Biosynthesis
  • Machine Learning in Materials Science
  • Chemical Synthesis and Analysis
  • Metabolomics and Mass Spectrometry Studies
  • Analytical Chemistry and Chromatography
  • Innovative Microfluidic and Catalytic Techniques Innovation

They have an extensive publication record, frequently contributing to journals such as:

  • Journal of Chemical Information and Modeling
  • Molecular Informatics
  • Zenodo (CERN European Organization for Nuclear Research)
  • Chemistry - A European Journal
  • bioRxiv (Cold Spring Harbor Laboratory)

Selected recent papers authored or co-authored by Dragos Horvath include:

  • CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity, 2020, Environmental Health Perspectives
  • A Close-up Look at the Chemical Space of Commercially Available Building Blocks for Medicinal Chemistry, 2021, Journal of Chemical Information and Modeling
  • Discovery of novel chemical reactions by deep generative recurrent neural network, 2021, Scientific Reports
  • Computational screening methodology identifies effective solvents for CO2 capture, 2022, Communications Chemistry
  • Will we ever be able to accurately predict solubility?, 2024, Scientific Data

Their collaborative network includes frequent co-authors such as:

  • Alexandre Varnek
  • Gilles Marcou
  • Alexey A. Orlov
  • Yuliana Zabolotna
  • Arkadii Lin

Best Publications

  • CERAPP: Collaborative Estrogen Receptor Activity Prediction Project

    Kamel Mansouri;Ahmed Abdelaziz;Aleksandra Rybacka;Alessandra Roncaglioni

  • Applicability domains for classification problems: Benchmarking of distance to models for Ames mutagenicity set.

    Iurii Sushko;Sergii Novotarskyi;Robert Körner;Anil Kumar Pandey

  • ISIDA - Platform for Virtual Screening Based on Fragment and Pharmacophoric Descriptors

    Alexandre Varnek;Denis Fourches;Dragos Horvath;Olga Klimchuk

  • CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity.

    Kamel Mansouri;Nicole Kleinstreuer;Ahmed M. Abdelaziz;Domenico Alberga

  • De Novo Molecular Design by Combining Deep Autoencoder Recurrent Neural Networks with Generative Topographic Mapping

    Boris Sattarov;Igor I. Baskin;Dragos Horvath;Gilles Marcou

  • ISIDA Property‐Labelled Fragment Descriptors

    Fiorella Ruggiu;Gilles Marcou;Alexandre Varnek;Dragos Horvath

  • Generative Topographic Mapping (GTM): Universal Tool for Data Visualization, Structure-Activity Modeling and Dataset Comparison.

    N. Kireeva;I. I. Baskin;I. I. Baskin;H. A. Gaspar;D. Horvath

  • Chemical data visualization and analysis with incremental generative topographic mapping: big data challenge.

    Héléna A. Gaspar;Igor I. Baskin;Igor I. Baskin;Igor I. Baskin;Gilles Marcou;Dragos Horvath

  • Pharmacophore-based virtual screening.

    Dragos Horvath

  • Predicting the Predictability: A Unified Approach to the Applicability Domain Problem of QSAR Models

    Unknown

  • Expert System for Predicting Reaction Conditions: The Michael Reaction Case

    G. Marcou;J. Aires de Sousa;D. A. R. S. Latino;A. de Luca

  • GTM-Based QSAR Models and Their Applicability Domains.

    H. A. Gaspar;I. I. Baskin;I. I. Baskin;I. I. Baskin;G. Marcou;D. Horvath

  • Discovery of novel chemical reactions by deep generative recurrent neural network

    William Bort;Igor I. Baskin;Igor I. Baskin;Igor I. Baskin;Timur Gimadiev;Artem Mukanov

  • Generative topographic mapping-based classification models and their applicability domain: application to the biopharmaceutics Drug Disposition Classification System (BDDCS).

    Héléna A. Gaspar;Gilles Marcou;Dragos Horvath;Alban Arault

  • An Evolutionary Optimizer of libsvm Models

    Dragos Horvath;J. B. Brown;Gilles Marcou;Alexandre Varnek

  • Computational screening methodology identifies effective solvents for CO2 capture

    Unknown

  • A parallel hybrid genetic algorithm for protein structure prediction on the computational grid

    A. A. Tantar;N. Melab;E. G. Talbi;B. Parent

  • Prediction of the Glass-Transition Temperatures of Linear Homo/Heteropolymers and Cross-Linked Epoxy Resins

    Chisa Higuchi;Chisa Higuchi;Dragos Horvath;Gilles Marcou;Kazunari Yoshizawa

  • Interpretability of SAR/QSAR Models of any Complexity by Atomic Contributions.

    Gilles Marcou;Dragos Horvath;V. Solov'ev;A. Arrault

  • Electrochemical properties of substituted 2-methyl-1,4-naphthoquinones: redox behavior predictions.

    Mourad Elhabiri;Pavel Sidorov;Pavel Sidorov;Elena Cesar‐Rodo;Gilles Marcou

  • Mapping of the Available Chemical Space versus the Chemical Universe of Lead-Like Compounds.

    Arkadii Lin;Dragos Horvath;Valentina Afonina;Valentina Afonina;Gilles Marcou

  • S4MPLE—Sampler for Multiple Protein-Ligand Entities: Methodology and Rigid-Site Docking Benchmarking

    Laurent Hoffer;Camelia Chira;Gilles Marcou;Alexandre Varnek

  • Mining chemical reactions using neighborhood behavior and condensed graphs of reactions approaches.

    Aurélie de Luca;Dragos Horvath;Gilles Marcou;Vitaly P. Solov'ev;Vitaly P. Solov'ev

  • Predictive Models for Kinetic Parameters of Cycloaddition Reactions

    Marta Glavatskikh;Marta Glavatskikh;Timur Madzhidov;Dragos Horvath;Ramil Nugmanov

  • Prediction of Activity Cliffs Using Condensed Graphs of Reaction Representations, Descriptor Recombination, Support Vector Machine Classification, and Support Vector Regression.

    Dragos Horvath;Gilles Marcou;Alexandre Varnek;Shilva Kayastha;Shilva Kayastha

  • Generative topographic mapping in drug design.

    Dragos Horvath;Gilles Marcou;Alexandre Varnek

  • Models for Identification of Erroneous Atom-to-Atom Mapping of Reactions Performed by Automated Algorithms

    Christophe Muller;Gilles Marcou;Dragos Horvath;João Aires-de-Sousa

  • Chemoinformatics-Driven Design of New Physical Solvents for Selective CO2 Absorption.

    Alexey A. Orlov;Daryna Yu. Demenko;Charles Bignaud;Alain Valtz

  • Chemography: Searching for Hidden Treasures.

    Yuliana Zabolotna;Arkadii I. Lin;Dragos Horvath;Gilles Marcou

  • Using self-organizing maps to accelerate similarity search

    Fanny Bonachera;Gilles Marcou;Natalia Kireeva;Alexandre Varnek

Frequent Co-Authors

Alexandre Varnek
Alexandre Varnek University of Strasbourg
Denis Fourches
Denis Fourches North Carolina State University
Igor V. Tetko
Igor V. Tetko Helmholtz Zentrum München
Roberto Todeschini
Roberto Todeschini University of Milano-Bicocca
Jürgen Bajorath
Jürgen Bajorath University of Bonn
Eugene N. Muratov
Eugene N. Muratov University of North Carolina at Chapel Hill
Vladimir Poroikov
Vladimir Poroikov Institute of Business & Medical Careers
Ruili Huang
Ruili Huang National Institutes of Health
Alexander Tropsha
Alexander Tropsha University of North Carolina at Chapel Hill
Jacques Haiech
Jacques Haiech University of Strasbourg

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