| 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 | 1849 | 2018 |
| Molecular de-novo design through deep reinforcement learning M Olivecrona, T Blaschke, O Engkvist, H Chen Journal of cheminformatics 9, 1-14, 2017 | 1295 | 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 | 469 | 2018 |
| 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 | 386 | 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), 1-14, 2019 | 187 | 2019 |
| Memory-assisted reinforcement learning for diverse molecular de novo design T Blaschke, O Engkvist, J Bajorath, H Chen Journal of cheminformatics 12 (1), 68, 2020 | 102 | 2020 |
| Machine learning distinguishes with high accuracy between pan-assay interference compounds that are promiscuous or represent dark chemical matter S Jasial, E Gilberg, T Blaschke, J Bajorath Journal of Medicinal Chemistry 61 (22), 10255-10264, 2018 | 34 | 2018 |
| Prediction of different classes of promiscuous and nonpromiscuous compounds using machine learning and nearest neighbor analysis T Blaschke, F Miljkovic, J Bajorath ACS Omega 4 (4), 6883-6890, 2019 | 25 | 2019 |
| Fine-tuning of a generative neural network for designing multi-target compounds T Blaschke, J Bajorath Journal of computer-aided molecular design, 1-9, 2021 | 21 | 2021 |
| Exploring the GDB-13 chemical space using deep generative models. J Cheminform 11: 20 J Arús-Pous, T Blaschke, S Ulander, JL Reymond, H Chen, O Engkvist | 15 | 2019 |
| Development of chromen-4-one derivatives as (ant) agonists for the lipid-activated G protein-coupled receptor GPR55 with tunable efficacy CT Schoeder, A Meyer, AB Mahardhika, D Thimm, T Blaschke, M Funke, ... ACS Omega 4 (2), 4276-4295, 2019 | 13 | 2019 |
| Faster and more diverse de novo molecular optimization with double-loop reinforcement learning using augmented SMILES EJ Bjerrum, C Margreitter, T Blaschke, S Kolarova, RLR de Castro Journal of Computer-Aided Molecular Design 37 (8), 373-394, 2023 | 10 | 2023 |
| Prediction of promiscuity cliffs using machine learning T Blaschke, C Feldmann, J Bajorath Molecular Informatics 40 (1), 2000196, 2021 | 7 | 2021 |
| Compound dataset and custom code for deep generative multi-target compound design T Blaschke, J Bajorath Future Science OA 7 (6), FSO715, 2021 | 3 | 2021 |
| An object-based semantic classification method of high resolution satellite imagery using ontology HY Gu, HT Li, L Yan, T Blaschke | 3 | 2016 |
| Compound Design Using Generative Neural Networks T Blaschke, J Bajorath | 1 | 2020 |
| Faster and more diverse de novo molecular optimization with double-loop reinforcement learning using augmented SMILES E Jannik Bjerrum, C Margreitter, T Blaschke, R Lopez-Rios de Castro arXiv e-prints, arXiv: 2210.12458, 2022 | | 2022 |
| 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), 1-14, 2019 | | 2019 |
| De novo molecular design using deep reinforcement learning methods HM Chen, M Olivercrona, T Blaschke, O Engkvist, T Kogej, C Tyrchan Abstracts of Papers of the American Chemical Society, 2018 | | 2018 |
Ola EngkvistAstraZeneca R&D Gothenburg Orcid:0000-0003-4970-6461Zweryfikowany adres z astrazeneca.com