Markov State Modeling (MSM) is used to predict both stationary and kinetic quantities on long timescales using a set of atomistic molecular dynamics simulations that are individually much shorter, thus addressing the well-known sampling problem in molecular dynamics simulation.

The MSM method enables an equilibrium binding model and its kinetic barrier heights to be reconstructed from multiple short off-equilibrium simulations. In any given simulated trajectory, only partial binding/unbinding transitions need to be observed. Trajectories are first geometrically clustered in a pre-defined conformational subspace from which a transition matrix (TM) is derived. Kinetic clustering of the eigenvectors of the TM enables metastable states to be identified and fluxes between them computed from transition path theory.

For examples of previously performed studies in which Markov State Modeling was the primary method used, see the following example cases:

- Protein conformational plasticity and complex ligand-binding kinetics explored by atomistic simulations and Markov models.
- On-the-Fly Learning and Sampling of Ligand Binding by High-Throughput Molecular Simulations.
- Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations.

Markov State Modeling was also used in the following examples: