World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

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

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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