| Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning F Noé*, S Olsson*, J Köhler*, H Wu Science 365 (6457), eaaw1147, 2019 | 172 | 2019 |
| Machine learning of coarse-grained molecular dynamics force fields J Wang, S Olsson, C Wehmeyer, A Perez, N Charron, G De Fabritiis, ... ACS Cent. Sci. 5 (5), 755-767, 2019 | 133 | 2019 |
| Combining experimental and simulation data of molecular processes via augmented Markov models S Olsson, H Wu, F Paul, C Clementi, F Noé Proceedings of the National Academy of Sciences 114 (31), 8265-8270, 2017 | 66 | 2017 |
| Inference of structure ensembles of flexible biomolecules from sparse, averaged data S Olsson, J Frellsen, W Boomsma, KV Mardia, T Hamelryck PloS one 8 (11), e79439, 2013 | 47 | 2013 |
| Probabilistic Determination of Native State Ensembles of Proteins. S Olsson, BR Vögeli, A Cavalli, W Boomsma, J Ferkinghoff-Borg, ... Journal of Chemical Theory and Computation, 2014 | 40 | 2014 |
| Camostat mesylate inhibits SARS-CoV-2 activation by TMPRSS2-related proteases and its metabolite GBPA exerts antiviral activity M Hoffmann, H Hofmann-Winkler, JC Smith, N Krüger, LK Sørensen, ... BioRxiv, 2020 | 37 | 2020 |
| PHAISTOS: A framework for Markov chain Monte Carlo simulation and inference of protein structure W Boomsma, J Frellsen, T Harder, S Bottaro, KE Johansson, P Tian, ... Journal of computational chemistry 34 (19), 1697-1705, 2013 | 36 | 2013 |
| Bayesian inference of protein ensembles from SAXS data LD Antonov, S Olsson, W Boomsma, T Hamelryck Physical Chemistry Chemical Physics 18 (8), 5832-5838, 2016 | 35 | 2016 |
| Introduction to Markov state modeling with the PyEMMA software [Article v1. 0] C Wehmeyer, MK Scherer, T Hempel, BE Husic, S Olsson, F Noé Living Journal of Computational Molecular Science 1 (1), 5965, 2018 | 31 | 2018 |
| The exact NOE as an alternative in ensemble structure determination B Vögeli, S Olsson, P Güntert, R Riek Biophysical journal 110 (1), 113-126, 2016 | 23 | 2016 |
| Mechanistic models of chemical exchange induced relaxation in protein NMR S Olsson, F Noé Journal of the American Chemical Society 139 (1), 200-210, 2017 | 22 | 2017 |
| Generative probabilistic models extend the scope of inferential structure determination S Olsson, W Boomsma, J Frellsen, S Bottaro, T Harder, J Ferkinghoff-Borg, ... Journal of Magnetic Resonance 213 (1), 182-186, 2011 | 22 | 2011 |
| Molecular dynamics of biomolecules through direct analysis of dipolar couplings S Olsson, D Ekonomiuk, J Sgrignani, A Cavalli Journal of the American Chemical Society 137 (19), 6270-6278, 2015 | 21 | 2015 |
| Molecular mechanism of inhibiting the SARS-CoV-2 cell entry facilitator TMPRSS2 with camostat and nafamostat T Hempel, L Raich, S Olsson, NP Azouz, AM Klingler, M Hoffmann, ... Chemical Science 12 (3), 983-992, 2021 | 17* | 2021 |
| The dynamic basis for signal propagation in human Pin1-WW S Olsson, D Strotz, B Vögeli, R Riek, A Cavalli Structure 24 (9), 1464-1475, 2016 | 17 | 2016 |
| Complementarity and congruence between exact NOEs and traditional NMR probes for spatial decoding of protein dynamics B Vögeli, S Olsson, R Riek, P Güntert Journal of structural biology 191 (3), 306-317, 2015 | 17 | 2015 |
| Molecular determinants for unphosphorylated STAT3 dimerization determined by integrative modeling J Sgrignani, S Olsson, D Ekonomiuk, D Genini, R Krause, CV Catapano, ... Biochemistry 54 (35), 5489-5501, 2015 | 15 | 2015 |
| The exact nuclear overhauser enhancement: recent advances PJ Nichols, A Born, MA Henen, D Strotz, J Orts, S Olsson, P Güntert, ... Molecules 22 (7), 1176, 2017 | 12 | 2017 |
| Dynamic graphical models of molecular kinetics S Olsson, F Noé Proceedings of the National Academy of Sciences 116 (30), 15001-15006, 2019 | 11 | 2019 |
| Coarse graining molecular dynamics with graph neural networks BE Husic, NE Charron, D Lemm, J Wang, A Pérez, M Majewski, A Krämer, ... The Journal of Chemical Physics 153 (19), 194101, 2020 | 10 | 2020 |
Noe FrankFU Berlin, GermanyVerified email at fu-berlin.de
