| Coarse graining molecular dynamics with graph neural networks BE Husic*, NE Charron*, D Lemm*, J Wang, A Pérez, M Majewski, ... The Journal of Chemical Physics 153 (19), 194101, 2020 | 150 | 2020 |
| SELFIES and the future of molecular string representations M Krenn, Q Ai, S Barthel, N Carson, A Frei, NC Frey, P Friederich, ... Patterns 3 (10), 2022 | 98 | 2022 |
| Machine learning based energy-free structure predictions of molecules, transition states, and solids D Lemm, GF Von Rudorff, OA Von Lilienfeld Nature Communications 12 (1), 4468, 2021 | 80 | 2021 |
| Identification and analysis of natural building blocks for evolution-guided fragment-based protein design N Ferruz, F Lobos, D Lemm, S Toledo-Patino, JA Farías-Rico, S Schmidt, ... Journal of molecular biology 432 (13), 3898-3914, 2020 | 38 | 2020 |
| Ab initio machine learning of phase space averages J Weinreich, D Lemm, GF von Rudorff, OA von Lilienfeld The Journal of Chemical Physics 157 (2), 2022 | 8 | 2022 |
| Improved decision making with similarity based machine learning: applications in chemistry D Lemm, GF von Rudorff, OA von Lilienfeld Machine Learning: Science and Technology 4 (4), 045043, 2023 | 7* | 2023 |
| PocketOptimizer 2.0: A modular framework for computer‐aided ligand‐binding design J Noske, JP Kynast, D Lemm, S Schmidt, B Höcker Protein Science 32 (1), e4516, 2023 | 6 | 2023 |
| Leruli.com, online molecular property predictions in real time and for free D Lemm, GF von Rudorff, OA von Lilienfeld https://leruli.com/, 2021 | 6 | 2021 |
| Impact of noise on inverse design: the case of NMR spectra matching D Lemm, GF von Rudorff, OA von Lilienfeld Digital Discovery 3 (1), 136-144, 2024 | | 2024 |
| Accelerating molecular and materials design with machine learning D Lemm doi.org/10.25365/thesis.75141, 2023 | | 2023 |
O. Anatole von LilienfeldClark & CIFAR AI chair at University of Toronto/Vector Institute/Technical University Berlin在 utoronto.ca 的电子邮件经过验证
