| Markov state models: From an art to a science BE Husic, VS Pande Journal of the American Chemical Society 140 (7), 2386-2396, 2018 | 792 | 2018 |
| PotentialNet for molecular property prediction EN Feinberg, D Sur, Z Wu, BE Husic, H Mai, Y Li, S Sun, J Yang, ... ACS central science 4 (11), 1520-1530, 2018 | 387 | 2018 |
| Unsupervised learning methods for molecular simulation data A Glielmo, BE Husic, A Rodriguez, C Clementi, F Noé, A Laio Chemical Reviews 121 (16), 9722-9758, 2021 | 290 | 2021 |
| MSMBuilder: statistical models for biomolecular dynamics MP Harrigan, MM Sultan, CX Hernández, BE Husic, P Eastman, ... Biophysical journal 112 (1), 10-15, 2017 | 275 | 2017 |
| Variational encoding of complex dynamics CX Hernández, HK Wayment-Steele, MM Sultan, BE Husic, VS Pande Physical Review E 97 (6), 062412, 2018 | 184 | 2018 |
| 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), 2020 | 168 | 2020 |
| Deeptime: a Python library for machine learning dynamical models from time series data M Hoffmann, M Scherer, T Hempel, A Mardt, B de Silva, BE Husic, S Klus, ... Machine Learning: Science and Technology 3 (1), 015009, 2021 | 104 | 2021 |
| Identification of simple reaction coordinates from complex dynamics RT McGibbon, BE Husic, VS Pande The Journal of Chemical Physics 146 (4), 2017 | 104 | 2017 |
| Optimized parameter selection reveals trends in Markov state models for protein folding BE Husic, RT McGibbon, MM Sultan, VS Pande The Journal of chemical physics 145 (19), 2016 | 77 | 2016 |
| Energy Landscapes for Proteins: From Single Funnels to Multifunctional Systems K Röder, JA Joseph, BE Husic, DJ Wales Advanced Theory and Simulations, 1800175, 2019 | 73 | 2019 |
| Variational selection of features for molecular kinetics MK Scherer, BE Husic, M Hoffmann, F Paul, H Wu, F Noé The Journal of chemical physics 150 (19), 2019 | 67 | 2019 |
| Machine learning implicit solvation for molecular dynamics Y Chen, A Krämer, NE Charron, BE Husic, C Clementi, F Noé The Journal of Chemical Physics 155 (8), 2021 | 58 | 2021 |
| Machine learning coarse-grained potentials of protein thermodynamics M Majewski, A Pérez, P Thölke, S Doerr, NE Charron, T Giorgino, ... Nature communications 14 (1), 5739, 2023 | 56 | 2023 |
| Modeling the mechanism of CLN025 beta-hairpin formation KA McKiernan, BE Husic, VS Pande The Journal of chemical physics 147 (10), 2017 | 55 | 2017 |
| Ward clustering improves cross-validated Markov state models of protein folding BE Husic, VS Pande Journal of chemical theory and computation 13 (3), 963-967, 2017 | 53 | 2017 |
| Multi-body effects in a coarse-grained protein force field J Wang, N Charron, B Husic, S Olsson, F Noé, C Clementi The Journal of Chemical Physics 154 (16), 2021 | 49 | 2021 |
| Osprey: Hyperparameter optimization for machine learning RT McGibbon, CX Hernández, MP Harrigan, S Kearnes, MM Sultan, ... Journal of Open Source Software 1 (5), 34, 2016 | 46 | 2016 |
| Kernel methods for detecting coherent structures in dynamical data S Klus, BE Husic, M Mollenhauer, F Noé Chaos: An Interdisciplinary Journal of Nonlinear Science 29 (12), 2019 | 33 | 2019 |
| Note: MSM lag time cannot be used for variational model selection BE Husic, VS Pande The Journal of chemical physics 147 (17), 2017 | 26 | 2017 |
| A minimum variance clustering approach produces robust and interpretable coarse-grained models BE Husic, KA McKiernan, HK Wayment-Steele, MM Sultan, VS Pande Journal of chemical theory and computation 14 (2), 1071-1082, 2018 | 23 | 2018 |