Advanced simulations with PLUMED: OPES and machine learning collective variables
Many biological processes occur on time scales longer than those accessible to molecular
dynamics simulations. Identifying collective variables (CVs) and introducing an external …
dynamics simulations. Identifying collective variables (CVs) and introducing an external …
A graph neural network-state predictive information bottleneck (GNN-SPIB) approach for learning molecular thermodynamics and kinetics
Molecular dynamics simulations offer detailed insights into atomic motions but face
timescale limitations. Enhanced sampling methods have addressed these challenges but …
timescale limitations. Enhanced sampling methods have addressed these challenges but …
Everything everywhere all at once, a probability-based enhanced sampling approach to rare events
The problem of studying rare events is central to many areas of computer simulations. In a
recent paper [Kang, P., et al., Nat. Comput. Sci. 4, 451-460, 2024], we have shown that a …
recent paper [Kang, P., et al., Nat. Comput. Sci. 4, 451-460, 2024], we have shown that a …
Acceleration with Interpretability: A Surrogate Model-Based Collective Variable for Enhanced Sampling
Most enhanced sampling methods facilitate the exploration of molecular free energy
landscapes by applying a bias potential along a reduced dimensional collective variable …
landscapes by applying a bias potential along a reduced dimensional collective variable …
Unbiased learning of protein conformational representation via unsupervised random forest
Accurate data representation is paramount in biophysics to capture the functionally relevant
motions of biomolecules. Traditional feature selection methods, while effective, often rely on …
motions of biomolecules. Traditional feature selection methods, while effective, often rely on …