| Counting on natural products for drug design T Rodrigues, D Reker, P Schneider, G Schneider Nature chemistry 8 (6), 531-541, 2016 | 937 | 2016 |
| 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 | 210 | 2014 |
| Active-learning strategies in computer-assisted drug discovery D Reker, G Schneider Drug discovery today 20 (4), 458-465, 2015 | 171 | 2015 |
| 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 | 140 | 2013 |
| 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 | 126 | 2014 |
| 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 | 81 | 2013 |
| “Inactive” ingredients in oral medications D Reker, SM Blum, C Steiger, KE Anger, JM Sommer, J Fanikos, ... Science Translational Medicine 11 (483), eaau6753, 2019 | 80 | 2019 |
| Active learning for computational chemogenomics D Reker, P Schneider, G Schneider, JB Brown Future Medicinal Chemistry 9 (4), 381-402, 2017 | 80 | 2017 |
| 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 | 75 | 2020 |
| 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 | 63 | 2015 |
| 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 | 56 | 2016 |
| Computational advances in combating colloidal aggregation in drug discovery D Reker, GJL Bernardes, T Rodrigues Nature chemistry 11 (5), 402-418, 2019 | 51 | 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 | 43 | 2015 |
| 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 | 42 | 2021 |
| 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 | 42 | 2019 |
| Multidimensional De Novo Design Reveals 5‐HT2B Receptor‐Selective Ligands T Rodrigues, N Hauser, D Reker, M Reutlinger, T Wunderlin, J Hamon, ... Angewandte Chemie International Edition 54 (5), 1551-1555, 2015 | 42 | 2015 |
| 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 | 41 | 2020 |
| Deorphaning the Macromolecular Targets of the Natural Anticancer Compound Doliculide G Schneider, D Reker, T Chen, K Hauenstein, P Schneider, KH Altmann Angewandte Chemie International Edition 55 (40), 12408-12411, 2016 | 40 | 2016 |
| Practical considerations for active machine learning in drug discovery D Reker Drug Discovery Today: Technologies 32, 73-79, 2019 | 38 | 2019 |
| Spotting and designing promiscuous ligands for drug discovery P Schneider, M Röthlisberger, D Reker, G Schneider Chemical Communications 52 (6), 1135-1138, 2016 | 38 | 2016 |
Gisbert SchneiderETH ZurichVerified email at ethz.ch
