Markov State Models: From an Art to a Science

Area of Science:

• Computational Chemistry
• Biophysics
• Statistical Mechanics

Background:

• Markov state models (MSMs) are widely used for analyzing molecular dynamics (MD) simulations.
• The field has evolved significantly over decades, with key developments in methodology and software.
• Early work focused on foundational concepts, while later studies established MSMs for MD analysis.

Purpose of the Study:

• To provide a historical overview of Markov state models (MSMs) for a general audience.
• To highlight key developments and turning points in MSM methodology and application.
• To showcase the impact of MSMs in understanding biological processes and facilitating drug discovery.

Main Methods:

• A chronological review of seminal papers and methodological advancements in MSMs.
• Emphasis on the development of a variational principle for objective MSM construction.
• Integration of best practices in model selection and open-source software utilization.

Main Results:

• The 2013 derivation of a variational principle marked a shift towards systematic and objective MSM building.
• Advances have enabled broad applications of MSMs in areas like protein folding, allostery, and ligand binding.
• Open-source software has democratized MSM analysis, making it accessible to a wider research community.

Conclusions:

• MSMs are a powerful and increasingly accessible tool for analyzing complex dynamical systems.
• Current research frontiers focus on further methodological improvements and novel applications.
• MSMs are poised to play a significant role in experimental design and accelerating drug discovery efforts.