| De Novo Design of Bioactive Small Molecules by Artificial Intelligence D Merk, L Friedrich, F Grisoni, G Schneider Molecular informatics 37 (1-2), 1700153, 2018 | 368 | 2018 |
| Generative molecular design in low data regimes M Moret, L Friedrich, F Grisoni, D Merk, G Schneider Nature Machine Intelligence 2 (3), 171-180, 2020 | 162 | 2020 |
| Tuning artificial intelligence on the de novo design of natural-product-inspired retinoid X receptor modulators D Merk, F Grisoni, L Friedrich, G Schneider Communications Chemistry 1 (1), 68, 2018 | 100 | 2018 |
| Ultrahigh-throughput screening enables efficient single-round oxidase remodelling A Debon, M Pott, R Obexer, AP Green, L Friedrich, AD Griffiths, D Hilvert Nature Catalysis 2 (9), 740-747, 2019 | 90 | 2019 |
| Mechanism of praziquantel action at a parasitic flatworm ion channel SK Park, L Friedrich, NA Yahya, CM Rohr, EG Chulkov, D Maillard, ... Science translational medicine 13 (625), eabj5832, 2021 | 75 | 2021 |
| From Complex Natural Products to Simple Synthetic Mimetics by Computational De Novo Design L Friedrich, T Rodrigues, CS Neuhaus, P Schneider, G Schneider Angewandte Chemie International Edition 55 (23), 6789-6792, 2016 | 50 | 2016 |
| Computer-assisted discovery of retinoid X receptor modulating natural products and isofunctional mimetics D Merk, F Grisoni, L Friedrich, E Gelzinyte, G Schneider Journal of medicinal chemistry 61 (12), 5442-5447, 2018 | 45 | 2018 |
| MELLODDY: cross pharma federated learning at unprecedented scale unlocks benefits in QSAR without compromising proprietary information W Heyndrickx, L Mervin, T Morawietz, N Sturm, L Friedrich, A Zalewski, ... | 39 | 2022 |
| Design of natural‐product‐inspired multitarget ligands by machine learning F Grisoni, D Merk, L Friedrich, G Schneider ChemMedChem 14 (12), 1129-1134, 2019 | 35 | 2019 |
| Context-enriched molecule representations improve few-shot drug discovery J Schimunek, P Seidl, L Friedrich, D Kuhn, F Rippmann, S Hochreiter, ... arXiv preprint arXiv:2305.09481, 2023 | 30 | 2023 |
| Matrix‐based molecular descriptors for prospective virtual compound screening F Grisoni, D Reker, P Schneider, L Friedrich, V Consonni, R Todeschini, ... Molecular Informatics 36 (1-2), 1600091, 2017 | 28 | 2017 |
| Industry-scale orchestrated federated learning for drug discovery M Oldenhof, G Ács, B Pejó, A Schuffenhauer, N Holway, N Sturm, ... Proceedings of the AAAI Conference on Artificial Intelligence 37 (13), 15576 …, 2023 | 25 | 2023 |
| Scaffold hopping from synthetic RXR modulators by virtual screening and de novo design D Merk, F Grisoni, L Friedrich, E Gelzinyte, G Schneider MedChemComm 9 (8), 1289-1292, 2018 | 21 | 2018 |
| Learning from Nature: From a Marine Natural Product to Synthetic Cyclooxygenase‐1 Inhibitors by Automated De Novo Design L Friedrich, G Cingolani, YH Ko, M Iaselli, M Miciaccia, MG Perrone, ... Advanced Science 8 (16), 2100832, 2021 | 20 | 2021 |
| Practical guidelines for the use of gradient boosting for molecular property prediction D Boldini, F Grisoni, D Kuhn, L Friedrich, SA Sieber Journal of Cheminformatics 15 (1), 73, 2023 | 14 | 2023 |
| Conformal efficiency as a metric for comparative model assessment befitting federated learning W Heyndrickx, A Arany, J Simm, A Pentina, N Sturm, L Humbeck, L Mervin, ... Artificial Intelligence in the Life Sciences 3, 100070, 2023 | 10 | 2023 |
| Tuning gradient boosting for imbalanced bioassay modelling with custom loss functions D Boldini, L Friedrich, D Kuhn, SA Sieber Journal of Cheminformatics 14 (1), 1-13, 2022 | 10 | 2022 |
| AIDDISON: Empowering Drug Discovery with AI/ML and CADD Tools in a Secure, Web-Based SaaS Platform A Rusinko, M Rezaei, L Friedrich, HP Buchstaller, D Kuhn, A Ghogare Journal of Chemical Information and Modeling 64 (1), 3-8, 2023 | 8 | 2023 |
| Von komplexen Naturstoffen zu synthetisch leicht zugänglichen Mimetika mithilfe von computergestütztem De‐novo‐Design L Friedrich, T Rodrigues, CS Neuhaus, P Schneider, G Schneider Angewandte Chemie 128 (23), 6901-6904, 2016 | 8 | 2016 |
| A generalized framework for embedding-based few-shot learning methods in drug discovery J Schimunek, L Friedrich, D Kuhn, F Rippmann, S Hochreiter, ... ELLIS Machine Learning for Molecule Discovery Workshop, 2021 | 7 | 2021 |
