An Introduction to Markov State Models and Their Application to Long Timescale Molecular Simulation

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Deep Generative Markov State Models (2018, methodology): “In stochastic dynamical systems, such as MD simulation, variants of Markov state models (MSMs) are commonly used [3, 24, 22].”
Molecular dynamics simulations and novel drug discovery (2018, methodology): “At present, increasing numbers of methods for trajectory analysis such as dynamic network analysis and Markov state model (MSM) analysis [90,168] are being intro-”
Predicting the locations of cryptic pockets from single protein structures using the PocketMiner graph neural network (2022, methodology): “Specifically, we launched 10 parallel simulations from the apo structure, then constructed a Markov state model (MSM) 32,33 of the conformational ensemble and prioritized structures for the next round of simulations using a ranking function that balances exploitation (i.e. prioritizing states with…”
Variational Approach for Learning Markov Processes from Time Series Data (2017, methodology): “In the past decades, a collection of closely related Markov modeling methods were developed in different fields, including Markov state models (MSMs) [44, 41, 5], Markov transition models [53], Ulam’s Galerkin method [11, 3, 16], blind-source separation [33, 55], the variational approach of conforma”
Coarse graining molecular dynamics with graph neural networks. (2020, background): “A great deal of research has gone into MSM theory and practice which is summarized in books 81,82 and recent reviews 56,83 .”