| Counting on natural products for drug design T Rodrigues, D Reker, P Schneider, G Schneider Nature chemistry 8 (6), 531-541, 2016 | 1215 | 2016 |
| Active-learning strategies in computer-assisted drug discovery D Reker, G Schneider Drug discovery today 20 (4), 458-465, 2015 | 255 | 2015 |
| Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus D Reker, T Rodrigues, P Schneider, G Schneider Proceedings of the National Academy of Sciences 111 (11), 4067-4072, 2014 | 237 | 2014 |
| Chemically Advanced Template Search (CATS) for Scaffold‐Hopping and Prospective Target Prediction for ‘Orphan’Molecules M Reutlinger, CP Koch, D Reker, N Todoroff, P Schneider, T Rodrigues, ... Molecular Informatics 32 (2), 133-138, 2013 | 166 | 2013 |
| Artificial intelligence for natural product drug discovery MW Mullowney, KR Duncan, SS Elsayed, N Garg, JJJ van der Hooft, ... Nature Reviews Drug Discovery 22 (11), 895-916, 2023 | 147 | 2023 |
| Artificial intelligence in chemistry and drug design N Brown, P Ertl, R Lewis, T Luksch, D Reker, N Schneider Journal of Computer-Aided Molecular Design 34 (7), 709-715, 2020 | 142 | 2020 |
| Revealing the macromolecular targets of complex natural products D Reker, AM Perna, T Rodrigues, P Schneider, M Reutlinger, B Mönch, ... Nature Chemistry 6 (12), 1072-1078, 2014 | 139 | 2014 |
| Computationally guided high-throughput design of self-assembling drug nanoparticles D Reker, Y Rybakova, AR Kirtane, R Cao, JW Yang, N Navamajiti, ... Nature nanotechnology 16 (6), 725-733, 2021 | 121 | 2021 |
| “Inactive” ingredients in oral medications D Reker, SM Blum, C Steiger, KE Anger, JM Sommer, J Fanikos, ... Science Translational Medicine 11 (483), eaau6753, 2019 | 117 | 2019 |
| Active learning for computational chemogenomics D Reker, P Schneider, G Schneider, JB Brown Future Medicinal Chemistry 9 (4), 381-402, 2017 | 102 | 2017 |
| Common non-epigenetic drugs as epigenetic modulators J Lötsch, G Schneider, D Reker, MJ Parnham, P Schneider, G Geisslinger, ... Trends in molecular medicine 19 (12), 742-753, 2013 | 96 | 2013 |
| Practical considerations for active machine learning in drug discovery D Reker Drug Discovery Today: Technologies 32, 73-79, 2019 | 86 | 2019 |
| Multi-objective active machine learning rapidly improves structure–activity models and reveals new protein–protein interaction inhibitors D Reker, P Schneider, G Schneider Chemical Science 7 (6), 3919-3927, 2016 | 81 | 2016 |
| Adaptive optimization of chemical reactions with minimal experimental information D Reker, EA Hoyt, GJL Bernardes, T Rodrigues Cell Reports Physical Science 1 (11), 100247, 2020 | 77 | 2020 |
| Computational advances in combating colloidal aggregation in drug discovery D Reker, GJL Bernardes, T Rodrigues Nature chemistry 11 (5), 402-418, 2019 | 70 | 2019 |
| Revealing the Macromolecular Targets of Fragment‐Like Natural Products T Rodrigues, D Reker, J Kunze, P Schneider, G Schneider Angewandte Chemie International Edition 54 (36), 10516-10520, 2015 | 70 | 2015 |
| Oral mRNA delivery using capsule-mediated gastrointestinal tissue injections A Abramson, AR Kirtane, Y Shi, G Zhong, JE Collins, S Tamang, K Ishida, ... Matter 5 (3), 975-987, 2022 | 67 | 2022 |
| Predicting protein-ligand interactions based on bow-pharmacological space and Bayesian additive regression trees L Li, CC Koh, D Reker, JB Brown, H Wang, NK Lee, H Liow, H Dai, ... Scientific reports 9 (1), 1-12, 2019 | 58 | 2019 |
| Fragment‐Based De Novo Design Reveals a Small‐Molecule Inhibitor of Helicobacter Pylori HtrA AM Perna, T Rodrigues, TP Schmidt, M Böhm, K Stutz, D Reker, B Pfeiffer, ... Angewandte Chemie International Edition 54 (35), 10244-10248, 2015 | 53 | 2015 |
| Machine Learning Uncovers Food-and Excipient-Drug Interactions D Reker, Y Shi, AR Kirtane, K Hess, GJ Zhong, E Crane, CH Lin, ... Cell Reports 30 (11), 3710-3716. e4, 2020 | 49 | 2020 |
