Brooke E. Husic
Cited by
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Markov state models: From an art to a science
BE Husic, VS Pande
Journal of the American Chemical Society 140 (7), 2386-2396, 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
MSMBuilder: statistical models for biomolecular dynamics
MP Harrigan, MM Sultan, CX Hernández, BE Husic, P Eastman, ...
Biophysical journal 112 (1), 10-15, 2017
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
Variational encoding of complex dynamics
CX Hernández, HK Wayment-Steele, MM Sultan, BE Husic, VS Pande
Physical Review E 97 (6), 062412, 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
Identification of simple reaction coordinates from complex dynamics
RT McGibbon, BE Husic, VS Pande
The Journal of Chemical Physics 146 (4), 2017
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
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
Energy Landscapes for Proteins: From Single Funnels to Multifunctional Systems
K Röder, JA Joseph, BE Husic, DJ Wales
Advanced Theory and Simulations, 1800175, 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
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
Modeling the mechanism of CLN025 beta-hairpin formation
KA McKiernan, BE Husic, VS Pande
The Journal of chemical physics 147 (10), 2017
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
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
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
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
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
Note: MSM lag time cannot be used for variational model selection
BE Husic, VS Pande
The Journal of chemical physics 147 (17), 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
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Articles 1–20