GraphVAMPNet, using graph neural networks and variational approach to Markov processes for dynamical modeling of biomolecules.

ABSTRACT: Finding a low dimensional representation of data from long-timescale trajectories of biomolecular processes, such as protein folding or ligand-receptor binding, is of fundamental importance, and kinetic models, such as Markov modeling, have proven useful in describing the kinetics of these systems. Recently, an unsupervised machine learning technique called VAMPNet was introduced to learn the low dimensional representation and the linear dynamical model in an end-to-end manner. VAMPNet is based on the variational approach for Markov processes and relies on neural networks to learn the coarse-grained dynamics. In this paper, we combine VAMPNet and graph neural networks to generate an end-to-end framework to efficiently learn high-level dynamics and metastable states from the long-timescale

SUBMITTER: Ghorbani M 

PROVIDER: S-EPMC9094994 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature