Ola Engkvist
Ola Engkvist
AstraZeneca R&D Gothenburg Orcid:0000-0003-4970-6461
Verified email at - Homepage
Cited by
Cited by
The rise of deep learning in drug discovery
H Chen, O Engkvist, Y Wang, M Olivecrona, T Blaschke
Drug discovery today 23 (6), 1241-1250, 2018
Molecular de-novo design through deep reinforcement learning
M Olivecrona, T Blaschke, O Engkvist, H Chen
Journal of cheminformatics 9 (1), 48, 2017
Application of Generative Autoencoder in De Novo Molecular Design
T Blaschke, M Olivecrona, O Engkvist, J Bajorath, H Chen
Molecular informatics 37 (1-2), 1700123, 2018
Molecular representations in AI-driven drug discovery: a review and practical guide
L David, A Thakkar, R Mercado, O Engkvist
Journal of Cheminformatics 12 (1), 1-22, 2020
Accurate intermolecular potentials obtained from molecular wave functions: Bridging the gap between quantum chemistry and molecular simulations
O Engkvist, PO Ĺstrand, G Karlström
Chemical Reviews 100 (11), 4087-4108, 2000
A de novo molecular generation method using latent vector based generative adversarial network
O Prykhodko, SV Johansson, P Kotsias, J Arús-Pous, EJ Bjerrum, ...
Journal of Cheminformatics 11 (74), 2019
Randomized SMILES strings improve the quality of molecular generative models
J Arús-Pous, SV Johansson, O Prykhodko, EJ Bjerrum, C Tyrchan, ...
Journal of cheminformatics 11 (1), 1-13, 2019
Structure and vibrational dynamics of the benzene dimer
V Špirko, O Engkvist, P Soldán, HL Selzle, EW Schlag, P Hobza
The Journal of chemical physics 111 (2), 572-582, 1999
Characterization of a conserved structural determinant controlling protein kinase sensitivity to selective inhibitors
S Blencke, B Zech, O Engkvist, Z Greff, L Őrfi, Z Horváth, G Kéri, A Ullrich, ...
Chemistry & biology 11 (5), 691-701, 2004
On the integration of in silico drug design methods for drug repurposing
E March-Vila, L Pinzi, N Sturm, A Tinivella, O Engkvist, H Chen, G Rastelli
Frontiers in pharmacology, 298, 2017
REINVENT 2.0: an AI tool for de novo drug design
T Blaschke, J Arús-Pous, H Chen, C Margreitter, C Tyrchan, O Engkvist, ...
Journal of chemical information and modeling 60 (12), 5918-5922, 2020
ExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomics
J Sun, N Jeliazkova, V Chupakhin, JF Golib-Dzib, O Engkvist, L Carlsson, ...
Journal of cheminformatics 9, 1-9, 2017
Computational prediction of chemical reactions: current status and outlook
O Engkvist, PO Norrby, N Selmi, Y Lam, Z Peng, EC Sherer, W Amberg, ...
Drug discovery today 23 (6), 1203-1218, 2018
AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning
S Genheden, A Thakkar, V Chadimová, JL Reymond, O Engkvist, ...
Journal of cheminformatics 12 (1), 70, 2020
Exploring the GDB-13 chemical space using deep generative models
J Arús-Pous, T Blaschke, S Ulander, JL Reymond, H Chen, O Engkvist
Journal of cheminformatics 11 (1), 20, 2019
Direct steering of de novo molecular generation with descriptor conditional recurrent neural networks
PC Kotsias, J Arús-Pous, H Chen, O Engkvist, C Tyrchan, EJ Bjerrum
Nature Machine Intelligence 2 (5), 254-265, 2020
Current and future roles of artificial intelligence in medicinal chemistry synthesis
TJ Struble, JC Alvarez, SP Brown, M Chytil, J Cisar, RL DesJarlais, ...
Journal of medicinal chemistry 63 (16), 8667-8682, 2020
Uracil dimer: potential energy and free energy surfaces. Ab initio beyond Hartree− Fock and empirical potential studies
M Kratochvíl, O Engkvist, J Šponer, P Jungwirth, P Hobza
The Journal of Physical Chemistry A 102 (35), 6921-6926, 1998
BIGCHEM: challenges and opportunities for big data analysis in chemistry
IV Tetko, O Engkvist, U Koch, JL Reymond, H Chen
Molecular informatics 35 (11-12), 615-621, 2016
Target prediction utilising negative bioactivity data covering large chemical space
LH Mervin, AM Afzal, G Drakakis, R Lewis, O Engkvist, A Bender
Journal of cheminformatics 7, 1-16, 2015
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