| 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 | 1614 | 2018 |
| Molecular de-novo design through deep reinforcement learning M Olivecrona, T Blaschke, O Engkvist, H Chen Journal of Cheminformatics 9, 48, 2017 | 1084 | 2017 |
| 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 | 506 | 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 | 427 | 2018 |
| 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 | 305 | 2019 |
| On evaluating molecular-docking methods for pose prediction and enrichment factors H Chen, PD Lyne, F Giordanetto, T Lovell, J Li Journal of chemical information and modeling 46 (1), 401-415, 2006 | 301 | 2006 |
| 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 | 283 | 2020 |
| Chemical predictive modelling to improve compound quality JG Cumming, AM Davis, S Muresan, M Haeberlein, H Chen Nature reviews Drug discovery 12 (12), 948-962, 2013 | 279 | 2013 |
| Randomized SMILES Strings Improve the Quality of Molecular Generative Models J Arús-Pous, S Johansson, O Ptykhodko, EJ Bjerrum, C Tyrchan, ... https://chemrxiv.org/articles …, 2019 | 273 | 2019 |
| 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 | 198 | 2017 |
| 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 | 193 | 2017 |
| Graph networks for molecular design R Mercado, T Rastemo, E Lindelöf, G Klambauer, O Engkvist, H Chen, ... Machine Learning: Science and Technology 2 (2), 025023, 2021 | 176 | 2021 |
| 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 | 168 | 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 | 162 | 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 | 148 | 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 | 142 | 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 | 133 | 2016 |
| Beyond size, ionization state, and lipophilicity: influence of molecular topology on absorption, distribution, metabolism, excretion, and toxicity for druglike compounds Y Yang, O Engkvist, A Llinàs, H Chen Journal of medicinal chemistry 55 (8), 3667-3677, 2012 | 126 | 2012 |
| MraY–antibiotic complex reveals details of tunicamycin mode of action JK Hakulinen, J Hering, G Brändén, H Chen, A Snijder, M Ek, ... Nature chemical biology 13 (3), 265-267, 2017 | 111 | 2017 |
| 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 | 92 | 2020 |
Ola EngkvistAstraZeneca R&D Gothenburg Orcid:0000-0003-4970-6461在 astrazeneca.com 的电子邮件经过验证
