Josep Arús-Pous
Josep Arús-Pous
Evinova (AstraZeneca)
Verified email at - Homepage
Cited by
Cited by
A de novo molecular generation method using latent vector based generative adversarial network
O Prykhodko, SV Johansson, PC Kotsias, J Arús-Pous, EJ Bjerrum, ...
Journal of Cheminformatics 11, 1-13, 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, 1-13, 2019
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
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
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, 1-14, 2019
SMILES-based deep generative scaffold decorator for de-novo drug design
J Arús-Pous, A Patronov, EJ Bjerrum, C Tyrchan, JL Reymond, H Chen, ...
Journal of cheminformatics 12, 1-18, 2020
Chemical space: big data challenge for molecular diversity
M Awale, R Visini, D Probst, J Arús-Pous, JL Reymond
Chimia 71 (10), 661-661, 2017
Applications of deep-learning in exploiting large-scale and heterogeneous compound data in industrial pharmaceutical research
L David, J Arús-Pous, J Karlsson, O Engkvist, EJ Bjerrum, T Kogej, ...
Frontiers in pharmacology 10, 1303, 2019
Virtual exploration of the ring systems chemical universe
R Visini, J Arús-Pous, M Awale, JL Reymond
Journal of chemical information and modeling 57 (11), 2707-2718, 2017
Exploring chemical space with machine learning
J Arús-Pous, M Awale, D Probst, JL Reymond
Chimia 73 (12), 1018-1018, 2019
The generated databases (GDBs) as a source of 3D-shaped building blocks for use in medicinal chemistry and drug discovery
K Meier, SO Bühlmann, J Arus Pous, JL Reymond
Chimia 74 (4), 241-246, 2020
A Potent and Selective Janus Kinase Inhibitor with a Chiral 3D‐Shaped Triquinazine Ring System from Chemical Space
K Meier, J Arús‐Pous, JL Reymond
Angewandte Chemie 133 (4), 2102-2105, 2021
Deep Learning Invades Drug Design and Synthesis: Medical Chemistry and Chemical Biology Highlights
J Arús-Pous, D Probst, JL Reymond
Chimia 72 (1-2), 70-70, 2018
Molecular De Novo Design Through Deep Generative Models
O Engkvist, J Arús-Pous, EJ Bjerrum, H Chen
Improving deep generative models with randomized smiles
J Arús-Pous, S Johansson, O Prykhodko, EJ Bjerrum, C Tyrchan, ...
International Conference on Artificial Neural Networks, 747-751, 2019
Comparison between SMILES-based differential neural computer and recurrent neural network architectures for de novo molecule design
SV Johansson, O Prykhodko, J Arús-Pous, O Engkvist, H Chen
Exploring the Chemical Space Using Enumerative and Deep Learning Approaches
J Arús-Pous
Universität Bern, Philosophisch-naturwissenschaftliche Fakultät, 2020
Artificial Intelligence: New Opportunities for Chemical Research?
T Luksch, HP Lüthi
Chimia 73 (12), 969, 2019
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