World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
42
Citations
17745
World Ranking
6355
National Ranking
208

Viviana Consonni 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 Viviana Consonni 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: 101 publications — 9th percentile

9% of scientists in this discipline score the same or lower.

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

Viviana Consonni 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 Viviana Consonni 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: 42 D-Index — 35th percentile

35% of scientists in this discipline score the same or lower.

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

Overview

Viviana Consonni is affiliated with the University of Milano-Bicocca in Italy. Their research spans multiple fields with a focus on computer science and chemistry, particularly at the intersection of computational methods and analytical sciences.

The primary fields of study in their work include:

  • Computer Science
  • Chemistry

Within these fields, they have contributed extensively to subfields such as:

  • Computational Theory and Mathematics
  • Molecular Biology
  • Analytical Chemistry
  • Spectroscopy
  • Artificial Intelligence

The main research topics covered in their publications include:

  • Computational Drug Discovery Methods
  • Analytical Chemistry and Chromatography
  • Machine Learning in Materials Science
  • Metabolomics and Mass Spectrometry Studies
  • Spectroscopy and Chemometric Analyses
  • Advanced Chemical Sensor Technologies
  • Analytical chemistry methods development

Viviana Consonni has contributed to multiple scientific journals, with frequent publications in the following venues:

  • Chemometrics and Intelligent Laboratory Systems
  • Environmental Health Perspectives
  • Molecules
  • Separations
  • Biomedical Science and Engineering

Some representative recent papers authored or co-authored by them include:

  • A MATLAB toolbox for multivariate regression coupled with variable selection (2021), Chemometrics and Intelligent Laboratory Systems
  • CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity (2020), Environmental Health Perspectives
  • CATMoS: Collaborative Acute Toxicity Modeling Suite (2021), Environmental Health Perspectives
  • Geographical identification of Chianti red wine based on ICP-MS element composition (2020), Food Chemistry
  • Consensus versus Individual QSARs in Classification: Comparison on a Large-Scale Case Study (2020), Journal of Chemical Information and Modeling

Frequent collaborators in their scientific work include:

  • Davide Ballabio
  • Roberto Todeschini
  • Francesca Grisoni
  • Fabio Gosetti
  • Cecile Valsecchi

Best Publications

  • Handbook of Molecular Descriptors

    Roberto Todeschini;Viviana Consonni

  • QSAR Modeling: Where have you been? Where are you going to?

    Artem Cherkasov;Eugene N. Muratov;Eugene N. Muratov;Denis Fourches;Alexandre Varnek

  • Molecular Descriptors for Chemoinformatics : Volume I : Alphabetical Listing / Volume II : Appendices, References

    Roberto Todeschini;Viviana Consonni

  • Classification tools in chemistry. Part 1: linear models. PLS-DA

    Davide Ballabio;Viviana Consonni

  • Molecular descriptors for chemoinformatics

    Roberto Todeschini;Viviana Consonni

  • DRAGON SOFTWARE: AN EASY APPROACH TO MOLECULAR DESCRIPTOR CALCULATIONS

    Andrea Mauri;Viviana Consonni;Manuela Pavan;Roberto Todeschini

  • Comparison of different approaches to define the applicability domain of QSAR models.

    F Sahigara;K Mansouri;D Ballabio;A Mauri

  • Structure/Response Correlations and Similarity/Diversity Analysis by GETAWAY Descriptors. 1. Theory of the Novel 3D Molecular Descriptors

    Viviana Consonni;Roberto Todeschini;Manuela Pavan

  • Evaluation of model predictive ability by external validation techniques

    Viviana Consonni;Davide Ballabio;Roberto Todeschini

  • Structure/Response Correlations and Similarity/Diversity Analysis by GETAWAY Descriptors. 2. Application of the Novel 3D Molecular Descriptors to QSAR/QSPR Studies

    Viviana Consonni;Roberto Todeschini;Manuela Pavan;Paola Gramatica

  • Quantitative structure-activity relationship models for ready biodegradability of chemicals.

    Kamel Mansouri;Tine Ringsted;Davide Ballabio;Roberto Todeschini

  • Similarity coefficients for binary chemoinformatics data: overview and extended comparison using simulated and real data sets.

    Roberto Todeschini;Viviana Consonni;Hua Xiang;John D. Holliday

  • The K correlation index: theory development and its application in chemometrics

    R. Todeschini;V. Consonni;A. Maiocchi

  • Modelling and prediction of soil sorption coefficients of non-ionic organic pesticides by molecular descriptors

    P Gramatica;M Corradi;V Consonni

  • Detecting bad regression models: multicriteria fitness functions in regression analysis

    Roberto Todeschini;Viviana Consonni;Andrea Mauri;Manuela Pavan

  • CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity.

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

  • Particle size, chemical composition, seasons of the year and urban, rural or remote site origins as determinants of biological effects of particulate matter on pulmonary cells.

    M.G. Perrone;Maurizio Gualtieri;V. Consonni;L. Ferrero

  • The Kohonen and CP-ANN toolbox: A collection of MATLAB modules for Self Organizing Maps and Counterpropagation Artificial Neural Networks

    Davide Ballabio;Viviana Consonni;Roberto Todeschini

  • CATMoS: Collaborative Acute Toxicity Modeling Suite.

    Kamel Mansouri;Agnes L. Karmaus;Jeremy Fitzpatrick;Grace Patlewicz

  • Defining a novel k-nearest neighbours approach to assess the applicability domain of a QSAR model for reliable predictions

    Faizan Sahigara;Davide Ballabio;Roberto Todeschini;Viviana Consonni

  • A novel variable reduction method adapted from space-filling designs

    Davide Ballabio;Viviana Consonni;Andrea Mauri;Magalie Claeys-Bruno

  • Genetic Algorithms for architecture optimisation of Counter-Propagation Artificial Neural Networks

    Davide Ballabio;Mahdi Vasighi;Viviana Consonni;Mohsen Kompany-Zareh

  • MobyDigs: software for regression and classification models by genetic algorithms

    Roberto Todeschini;Viviana Consonni;Andrea Mauri;Manuela Pavan

  • In Silico Prediction of Cytochrome P450-Drug Interaction: QSARs for CYP3A4 and CYP2C9

    Serena Nembri;Francesca Grisoni;Viviana Consonni;Roberto Todeschini

  • Machine Learning Consensus To Predict the Binding to the Androgen Receptor within the CoMPARA Project.

    Francesca Grisoni;Viviana Consonni;Davide Ballabio

  • Integrated QSAR Models to Predict Acute Oral Systemic Toxicity

    Davide Ballabio;Francesca Grisoni;Viviana Consonni;Roberto Todeschini

  • A QSTR-based expert system to predict sweetness of molecules

    Cristian Rojas;Roberto Todeschini;Davide Ballabio;Andrea Mauri

  • CAIMAN (Classification and Influence Matrix Analysis) : A new approach to the classification based on leverage-scaled functions

    R. Todeschini;D. Ballabio;V. Consonni;A. Mauri

  • Steric Control of Conductivity in Highly Conjugated Polythiophenes

    T. Benincori;V. Consonni;P. Gramatica;T. Pilati

  • Resolution of mixtures of three nonsteroidal anti-inflammatory drugs by fluorescence using partial least squares multivariate calibration with previous wavelength selection by Kohonen artificial neural networks.

    L F Capitán-Vallvey;N Navas;M Del Olmo;V Consonni

  • Prediction of aromatic amines mutagenicity from theoretical molecular descriptors.

    P Gramatica;V Consonni;M Pavan

Frequent Co-Authors

Roberto Todeschini
Roberto Todeschini University of Milano-Bicocca
Davide Ballabio
Davide Ballabio University of Milano-Bicocca
Eugene N. Muratov
Eugene N. Muratov University of North Carolina at Chapel Hill
Alexandre Varnek
Alexandre Varnek University of Strasbourg
Denis Fourches
Denis Fourches North Carolina State University
Igor V. Tetko
Igor V. Tetko Helmholtz Zentrum München
Alexander Tropsha
Alexander Tropsha University of North Carolina at Chapel Hill
Sean Ekins
Sean Ekins University of Arizona
Thomas Hartung
Thomas Hartung Johns Hopkins University

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