Shuzhe Wang, Gerhard König, Hans-Jörg Roth, Marianne Fouché, Stephane Rodde, Sereina Riniker.Journal of Chemical Theory and Computation 2021, 17 Time-Lagged Independent Component Analysis of Random Walks and Protein Dynamics. Markov State Models to Study the Functional Dynamics of Proteins in the Wake of Machine Learning. Konovalov, Ilona Christy Unarta, Siqin Cao, Eshani C. This article is cited by 389 publications. In this work, we demonstrate the features of the software and show new methodological concepts and results produced by PyEMMA. Plotting functions to produce a manuscript-ready presentation of the results are available. We have derived a systematic and accurate way to coarse-grain MSMs to few states and to illustrate the structures of the metastable states of the system. PyEMMA offers a wealth of analysis functions such that the user can conveniently compute molecular observables of interest. Systematic model validation and error calculation methods are provided. PyEMMA can read all common molecular dynamics data formats, helps in the selection of input features, provides easy access to dimension reduction algorithms such as principal component analysis (PCA) and time-lagged independent component analysis (TICA) and clustering algorithms such as k-means, and contains estimators for MSMs, hidden Markov models, and several other models. In this work we present the open-source Python package PyEMMA ( ) that provides accurate and efficient algorithms for kinetic model construction. However, the estimation, validation, and analysis of such models is far from trivial and involves sophisticated and often numerically sensitive methods. Markov (state) models (MSMs) and related models of molecular kinetics have recently received a surge of interest as they can systematically reconcile simulation data from either a few long or many short simulations and allow us to analyze the essential metastable structures, thermodynamics, and kinetics of the molecular system under investigation.

Hidden markov model scikit learn