World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
51
Citations
11896
World Ranking
5290
National Ranking
318

Ola Engkvist publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Ola Engkvist sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 250 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 560 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 158 publications — 30th percentile

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

The last bar groups every scientist with 991 publications or more.

Ola Engkvist D-index placement in Computer Science in 2026

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

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 51 D-Index — 63rd percentile

63% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Ola Engkvist is affiliated with AstraZeneca in the United Kingdom and has an extensive publication record in the domains of computer science and biochemistry, genetics, and molecular biology. Their research spans key areas within computational theory and mathematics, molecular biology, materials chemistry, artificial intelligence, and biomedical engineering.

The primary topics engaged by Engkvist focus on computational drug discovery methods, machine learning applications in materials science, protein structure and dynamics, chemical synthesis and analysis, innovative microfluidic and catalytic techniques, genetics, bioinformatics, biomedical research, and bioinformatics and genomic networks.

Frequent co-authors include Esben Jannik Bjerrum, Lewis Mervin, Atanas Patronov, Jon Paul Janet, and Christian Tyrchan, indicating collaborative work in intersecting fields.

Engkvist's publication venues demonstrate a concentration in specialized journals and platforms related to cheminformatics and computational chemistry. The most prominent publication venues are:

  • Journal of Cheminformatics
  • arXiv (Cornell University)
  • Journal of Chemical Information and Modeling
  • Zenodo (CERN European Organization for Nuclear Research)
  • Machine Learning Science and Technology

Selected recent papers illustrate the scope and nature of Engkvist's scholarly output:

  • "Molecular representations in AI-driven drug discovery: a review and practical guide" (2020), Journal of Cheminformatics
  • "REINVENT 2.0: An AI Tool for De Novo Drug Design" (2020), Journal of Chemical Information and Modeling
  • "AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning" (2020), Journal of Cheminformatics
  • "Building attention and edge message passing neural networks for bioactivity and physical-chemical property prediction" (2020), Journal of Cheminformatics
  • "Direct steering of de novo molecular generation with descriptor conditional recurrent neural networks" (2020), Nature Machine Intelligence

These works reflect contributions to AI-driven methodologies for drug discovery, retrosynthetic planning, and neural network architectures for predicting bioactivity and chemical properties, integrating artificial intelligence with chemistry and molecular biology.

Best Publications

  • The rise of deep learning in drug discovery.

    Hongming Chen;Ola Engkvist;Yinhai Wang;Marcus Olivecrona

  • Molecular de-novo design through deep reinforcement learning

    Marcus Olivecrona;Thomas Blaschke;Ola Engkvist;Hongming Chen

  • Molecular representations in AI-driven drug discovery: a review and practical guide

    Laurianne David;Amol Thakkar;Amol Thakkar;Rocío Mercado;Ola Engkvist

  • Application of Generative Autoencoder in De Novo Molecular Design.

    Thomas Blaschke;Marcus Olivecrona;Ola Engkvist;Jürgen Bajorath

  • REINVENT 2.0: An AI Tool for De Novo Drug Design.

    Thomas Blaschke;Josep Arús-Pous;Josep Arús-Pous;Hongming Chen;Christian Margreitter

  • A de novo molecular generation method using latent vector based generative adversarial network

    Oleksii Prykhodko;Oleksii Prykhodko;Simon Viet Johansson;Simon Viet Johansson;Panagiotis-Christos Kotsias;Josep Arús-Pous;Josep Arús-Pous

  • Randomized SMILES strings improve the quality of molecular generative models

    Josep Arús-Pous;Josep Arús-Pous;Simon Viet Johansson;Oleksii Prykhodko;Esben Jannik Bjerrum

  • AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning

    Samuel Genheden;Amol Thakkar;Amol Thakkar;Veronika Chadimová;Jean-Louis Reymond

  • ExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomics

    Jiangming Sun;Nina Jeliazkova;Vladimir Chupakhin;Jose-Felipe Golib-Dzib

  • Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis.

    Thomas J. Struble;Juan C. Alvarez;Scott P. Brown;Milan Chytil

  • On the integration of in silico drug design methods for drug repurposing

    Eric March-Vila;Luca Pinzi;Noé Sturm;Annachiara Tinivella

  • Building attention and edge message passing neural networks for bioactivity and physical–chemical property prediction

    Michael Withnall;Edvard Lindelöf;Ola Engkvist;Hongming Chen

  • Direct steering of de novo molecular generation with descriptor conditional recurrent neural networks

    Panagiotis-Christos Kotsias;Josep Arús-Pous;Josep Arús-Pous;Hongming Chen;Ola Engkvist

  • SMILES-based deep generative scaffold decorator for de-novo drug design

    Josep Arús-Pous;Josep Arús-Pous;Atanas Patronov;Esben Jannik Bjerrum;Christian Tyrchan

  • Computational prediction of chemical reactions: current status and outlook.

    Ola Engkvist;Per-Ola Norrby;Nidhal Selmi;Yu-hong Lam

  • Exploring the GDB-13 chemical space using deep generative models

    Josep Arús-Pous;Josep Arús-Pous;Thomas Blaschke;Thomas Blaschke;Silas Ulander;Jean-Louis Reymond

  • Retrosynthetic accessibility score (RAscore) – rapid machine learned synthesizability classification from AI driven retrosynthetic planning

    Amol Thakkar;Amol Thakkar;Veronika Chadimová;Esben Jannik Bjerrum;Ola Engkvist

  • Graph networks for molecular design

    Rocío Mercado;Tobias Rastemo;Tobias Rastemo;Edvard Lindelöf;Edvard Lindelöf;Günter Klambauer

  • Datasets and their influence on the development of computer assisted synthesis planning tools in the pharmaceutical domain

    Amol Thakkar;Amol Thakkar;Thierry Kogej;Jean-Louis Reymond;Ola Engkvist

  • Target prediction utilising negative bioactivity data covering large chemical space

    Lewis H. Mervin;Avid M. Afzal;Georgios Drakakis;Richard Lewis

  • BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry

    Igor V. Tetko;Ola Engkvist;Uwe Koch;Jean-Louis Reymond

  • Molecular modeling of the second extracellular loop of G-protein coupled receptors and its implication on structure-based virtual screening.

    Chris de Graaf;Nicolas Foata;Ola Engkvist;Didier Rognan

Frequent Co-Authors

Andreas Bender
Andreas Bender University of Cambridge
Günter Klambauer
Günter Klambauer Johannes Kepler University of Linz
Jürgen Bajorath
Jürgen Bajorath University of Bonn
Matthew Cotten
Matthew Cotten Wellcome Sanger Institute
Igor V. Tetko
Igor V. Tetko Helmholtz Zentrum München
Stephen J. Moss
Stephen J. Moss Tufts University
Nicholas J. Brandon
Nicholas J. Brandon Neumora Therapeutics Inc
Janet M. Thornton
Janet M. Thornton European Bioinformatics Institute
Kim E. Hammond-Kosack
Kim E. Hammond-Kosack Rothamsted Research

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