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MSM/RD: Coupling Markov state models of molecular kinetics with reaction-diffusion simulations
Manuel Dibak, Mauricio J. del Razo, David De Sancho +2
Molecular dynamics (MD) simulations can model the interactions between macromolecules with high spatiotemporal resolution but at a high computational cost. By combining high-throug…
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics
Christoph Wehmeyer, Frank Noé
Inspired by the success of deep learning techniques in the physical and chemical sciences, we apply a modification of an autoencoder type deep neural network to the task of dimensi…
An efficient multi-scale Green's Functions Reaction Dynamics scheme
Luigi Sbailò, Frank Noé
Molecular Dynamics - Green's Functions Reaction Dynamics (MD-GFRD) is a multiscale simulation method for particle dynamics or particle-based reaction-diffusion dynamics that is sui…
Particle-based membrane model for mesoscopic simulation of cellular dynamics
Mohsen Sadeghi, Thomas R. Weikl, Frank Noé
We present a simple and computationally efficient coarse-grained and solvent-free model for simulating lipid bilayer membranes. In order to be used in concert with particle-based r…
VAMPnets: Deep learning of molecular kinetics
Andreas Mardt, Luca Pasquali, Hao Wu +1
There is an increasing demand for computing the relevant structures, equilibria and long-timescale kinetics of biomolecular processes, such as protein-drug binding, from high-throu…
Data-driven model reduction and transfer operator approximation
Stefan Klus, Feliks Nüske, Péter Koltai +4
In this review paper, we will present different data-driven dimension reduction techniques for dynamical systems that are based on transfer operator theory as well as methods to ap…