Markov State Models in Drug Design

This chapter explains the different ways in which Markov State Models (MSMs) can be helpful in structure‐based drug design. MSMs are constructed from the time series of molecular dynamics (MD), which can be generated by classical MD simulations. Several features of the MSMs can be utilized for rational drug design. The discretization of a validated MSM is particularly suited to extract meaningful representatives from the conformational ensemble, because the discretization yields a small number of microstates and mirrors the features of the free energy landscape. Long‐lived conformations consist of a set of microstates which show high transition rates within the set and low transition rates to microstates outside of the set. The Bayesian agglomerative clustering engine (BACE) algorithm uses the observed transition counts to extract long‐lived conformations from an MSM. By iteratively merging microstates according to the Bayes factor and recalculating the Bayes‐factor matrix, the algorithm yields an aggregation of the microstates into long‐lived conformations.