239 citations · 497 across the 4 of their papers we have counts for
5 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…
TorchMD: A deep learning framework for molecular simulations
Stefan Doerr, Maciej Majewsk, Adrià Pérez +5
Molecular dynamics simulations provide a mechanistic description of molecules by relying on empirical potentials. The quality and transferability of such potentials can be improved…
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…
AdaptiveBandit: A multi-armed bandit framework for adaptive sampling in molecular simulations
Adrià Pérez, Pablo Herrera-Nieto, Stefan Doerr +1
Sampling from the equilibrium distribution has always been a major problem in molecular simulations due to the very high dimensionality of conformational space. Over several decade…
Simulations meet Machine Learning in Structural Biology
Adrià Pérez, Gerard Martínez-Rosell, Gianni De Fabritiis
Classical molecular dynamics (MD) simulations will be able to reach sampling in the second timescale within five years, producing petabytes of simulation data at current force fiel…