198 citations · 853 across the 15 of their papers we have counts for
15 papers
Navigating protein landscapes with a machine-learned transferable coarse-grained model
Nicholas E. Charron, Felix Musil, Andrea Guljas +18
The most popular and universally predictive protein simulation models employ all-atom molecular dynamics (MD), but they come at extreme computational cost. The development of a uni…
Machine Learning Coarse-Grained Potentials of Protein Thermodynamics
Maciej Majewski, Adrià Pérez, Philipp Thölke +7
A generalized understanding of protein dynamics is an unsolved scientific problem, the solution of which is critical to the interpretation of the structure-function relationships t…
Deeptime: a Python library for machine learning dynamical models from time series data
Moritz Hoffmann, Martin Scherer, Tim Hempel +8
Generation and analysis of time-series data is relevant to many quantitative fields ranging from economics to fluid mechanics. In the physical sciences, structures such as metastab…
Machine Learning Implicit Solvation for Molecular Dynamics
Yaoyi Chen, Andreas Krämer, Nicholas E. Charron +3
Accurate modeling of the solvent environment for biological molecules is crucial for computational biology and drug design. A popular approach to achieve long simulation time scale…
Coarse Graining Molecular Dynamics with Graph Neural Networks
Brooke E. Husic, Nicholas E. Charron, Dominik Lemm +9
Coarse graining enables the investigation of molecular dynamics for larger systems and at longer timescales than is possible at atomic resolution. However, a coarse graining model…
Deflation reveals dynamical structure in nondominant reaction coordinates
Brooke E. Husic, Frank Noé
The output of molecular dynamics simulations is high-dimensional, and the degrees of freedom among the atoms are related in intricate ways. Therefore, a variety of analysis framewo…