| 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 | 1572 | 2018 |
| Molecular de-novo design through deep reinforcement learning M Olivecrona, T Blaschke, O Engkvist, H Chen Journal of cheminformatics 9, 1-14, 2017 | 1051 | 2017 |
| Automating drug discovery G Schneider Nature reviews drug discovery 17 (2), 97-113, 2018 | 633 | 2018 |
| QSAR without borders EN Muratov, J Bajorath, RP Sheridan, IV Tetko, D Filimonov, V Poroikov, ... Chemical Society Reviews 49 (11), 3525-3564, 2020 | 589 | 2020 |
| 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 | 422 | 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), 56, 2020 | 358 | 2020 |
| Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions R Rodríguez-Pérez, J Bajorath Journal of computer-aided molecular design 34 (10), 1013-1026, 2020 | 302 | 2020 |
| 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 | 288 | 2019 |
| State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis IV Tetko, P Karpov, R Van Deursen, G Godin Nature communications 11 (1), 5575, 2020 | 267 | 2020 |
| 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 | 262 | 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 | 259 | 2020 |
| Interpretation of compound activity predictions from complex machine learning models using local approximations and shapley values R Rodríguez-Pérez, J Bajorath Journal of medicinal chemistry 63 (16), 8761-8777, 2019 | 232 | 2019 |
| 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 | 211 | 2020 |
| 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 8, 298, 2017 | 189 | 2017 |
| Transformer-CNN: Swiss knife for QSAR modeling and interpretation P Karpov, G Godin, IV Tetko Journal of Cheminformatics 12 (1), 1-12, 2020 | 177 | 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 | 164 | 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 | 158 | 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 | 143 | 2020 |
| Building attention and edge message passing neural networks for bioactivity and physical–chemical property prediction M Withnall, E Lindelöf, O Engkvist, H Chen Journal of Cheminformatics 12 (1), 1, 2020 | 138 | 2020 |
| 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 | 130 | 2016 |
Ola EngkvistAstraZeneca R&D Gothenburg Orcid:0000-0003-4970-6461Verified email at astrazeneca.com
