| Markov models of molecular kinetics: Generation and validation JH Prinz, H Wu, M Sarich, B Keller, M Senne, M Held, JD Chodera, ... The Journal of chemical physics 134, 174105, 2011 | 1361 | 2011 |
| Major histocompatibility complex (MHC) class I and MHC class II proteins: conformational plasticity in antigen presentation M Wieczorek, ET Abualrous, J Sticht, M Álvaro-Benito, S Stolzenberg, ... Frontiers in immunology 8, 292, 2017 | 1284 | 2017 |
| PyEMMA 2: A software package for estimation, validation, and analysis of Markov models MK Scherer, B Trendelkamp-Schroer, F Paul, G Pérez-Hernández, ... Journal of chemical theory and computation 11 (11), 5525-5542, 2015 | 1118 | 2015 |
| Identification of slow molecular order parameters for Markov model construction G Pérez-Hernández, F Paul, T Giorgino, G De Fabritiis, F Noé The Journal of chemical physics 139 (1), 2013 | 1042 | 2013 |
| Constructing the equilibrium ensemble of folding pathways from short off-equilibrium simulations F Noé, C Schütte, E Vanden-Eijnden, L Reich, TR Weikl Proceedings of the National Academy of Sciences 106 (45), 19011, 2009 | 930 | 2009 |
| Machine learning for molecular simulation F Noé, A Tkatchenko, KR Müller, C Clementi Annual review of physical chemistry 71 (1), 361-390, 2020 | 850 | 2020 |
| Markov state models of biomolecular conformational dynamics JD Chodera, F Noé Current opinion in structural biology 25, 135-144, 2014 | 799 | 2014 |
| 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 | 772 | 2019 |
| VAMPnets for deep learning of molecular kinetics A Mardt, L Pasquali, H Wu, F Noé Nature communications 9 (1), 5, 2018 | 663 | 2018 |
| An introduction to Markov state models and their application to long timescale molecular simulation GR Bowman, VS Pande, F Noé Springer Science & Business Media, 2013 | 637 | 2013 |
| Deep-neural-network solution of the electronic Schrödinger equation J Hermann, Z Schätzle, F Noé Nature Chemistry 12 (10), 891-897, 2020 | 598 | 2020 |
| Transition networks for modeling the kinetics of conformational change in macromolecules F Noé, S Fischer Current opinion in structural biology 18 (2), 154-162, 2008 | 538 | 2008 |
| Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations R Winter, F Montanari, F Noé, DA Clevert Chemical science 10 (6), 1692-1701, 2019 | 504 | 2019 |
| Machine learning of coarse-grained molecular dynamics force fields J Wang, S Olsson, C Wehmeyer, A Pérez, NE Charron, G De Fabritiis, ... ACS central science 5 (5), 755-767, 2019 | 498 | 2019 |
| Hierarchical analysis of conformational dynamics in biomolecules: Transition networks of metastable states F Noé, I Horenko, C Schütte, JC Smith The Journal of chemical physics 126, 155102, 2007 | 473 | 2007 |
| Spatiotemporal control of endocytosis by phosphatidylinositol-3, 4-bisphosphate Y Posor, M Eichhorn-Gruenig, D Puchkov, J Schöneberg, A Ullrich, ... Nature 499 (7457), 233-237, 2013 | 468 | 2013 |
| Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics C Wehmeyer, F Noé The Journal of chemical physics 148 (24), 2018 | 437 | 2018 |
| Protein conformational plasticity and complex ligand-binding kinetics explored by atomistic simulations and Markov models N Plattner, F Noé Nature communications 6 (1), 7653, 2015 | 432 | 2015 |
| HTMD: high-throughput molecular dynamics for molecular discovery S Doerr, MJ Harvey, F Noé, G De Fabritiis Journal of chemical theory and computation 12 (4), 1845-1852, 2016 | 418 | 2016 |
| Crystal structure of nucleotide-free dynamin K Faelber, Y Posor, S Gao, M Held, Y Roske, D Schulze, V Haucke, F Noé, ... Nature, 2011 | 373 | 2011 |
Hao WuFree university of Berlin確認したメール アドレス: zedat.fu-berlin.de