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Dominik Lemm
Dominik Lemm
Department of Physics, University of Vienna
Bestätigte E-Mail-Adresse bei univie.ac.at
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Zitiert von
Zitiert von
Jahr
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
1782020
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
1502022
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
942021
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
412020
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
11*2023
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
112022
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
82023
Leruli.com, online molecular property predictions in real time and for free
D Lemm, GF von Rudorff, OA von Lilienfeld
https://leruli.com/, 2021
52021
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
22024
Accelerating molecular and materials design with machine learning
D Lemm
doi.org/10.25365/thesis.75141, 2023
2023
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