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Simon Viet Johansson
Simon Viet Johansson
Industrial Ph.D student, AstraZeneca AB & Chalmers University of Technology
Verified email at student.chalmers.se
Title
Cited by
Cited by
Year
Molecular sets (MOSES): a benchmarking platform for molecular generation models
D Polykovskiy, A Zhebrak, B Sanchez-Lengeling, S Golovanov, O Tatanov, ...
Frontiers in pharmacology 11, 565644, 2020
4702020
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
2872019
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
2602019
Uncertainty quantification in drug design
LH Mervin, S Johansson, E Semenova, KA Giblin, O Engkvist
Drug discovery today 26 (2), 474-489, 2021
592021
Artificial intelligence and automation in computer aided synthesis planning
A Thakkar, S Johansson, K Jorner, D Buttar, JL Reymond, O Engkvist
Reaction chemistry & engineering 6 (1), 27-51, 2021
512021
AI-assisted synthesis prediction
S Johansson, A Thakkar, T Kogej, E Bjerrum, S Genheden, T Bastys, ...
Drug Discovery Today: Technologies 32, 65-72, 2019
452019
Randomized SMILES strings improve the quality of molecular generative models. J Cheminform 11: 71
J Arús-Pous, SV Johansson, O Prykhodko, EJ Bjerrum, C Tyrchan, ...
122019
Molecular sets (moses): a benchmarking platform for molecular generation models. Front Pharmacol
D Polykovskiy, A Zhebrak, B Sanchez-Lengeling, S Golovanov, O Tatanov, ...
112020
Using active learning to develop machine learning models for reaction yield prediction
S Viet Johansson, H Gummesson Svensson, E Bjerrum, A Schliep, ...
Molecular Informatics 41 (12), 2200043, 2022
102022
A de novo molecular generation method using latent vector based generative adversarial network. J Cheminform 11: 74
O Prykhodko, SV Johansson, PC Kotsias, J Arús-Pous, EJ Bjerrum, ...
92019
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
22019
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
22019
De novo generated combinatorial library design
SV Johansson, MH Chehreghani, O Engkvist, A Schliep
Digital Discovery 3 (1), 122-135, 2024
12024
Diverse Data Expansion with Semi-Supervised k-Determinantal Point Processes
S Johansson, O Engkvist, MH Chehreghani, A Schliep
2023 IEEE International Conference on Big Data (BigData), 5260-5265, 2023
2023
Intelligent data acquisition for drug design through combinatorial library design
S Johansson
PQDT-Global, 2023
2023
Differentiable Neural Computers for in silico molecular design: Benchmarks of architectures in generative modeling of molecules
O PRYKHODKO, S JOHANSSON
2019
Additional methods
J Arús-Pous, S Johansson, O Prykhodko, EJ Bjerrum, C Tyrchan, ...
ratio 2, 1,564,030, 0
Supplementary methods
J Arús-Pous, S Johansson, O Prykhodko, EJ Bjerrum, C Tyrchan, ...
ratio 2, 1,564,030, 0
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