PyEMMA 2: A Software Package for Estimation, Validation, and Analysis of Markov Models.

By Martin K. Scherer, Benjamin Trendelkamp-Schroer, Fabian Paul, G. Pérez-Hernández, Moritz Hoffmann, Nuria Plattner, Christoph Wehmeyer, Jan-Hendrik Prinz, Frank No'e. Journal of Chemical Theory and Computation, 2015

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Coarse graining molecular dynamics with graph neural networks. (2020, methodology): “Open source software packages facilitating MSM analyses include PyEMMA 86,87 , MSMBuilder 88 , and Enspara 89 .”
VAMPnets for deep learning of molecular kinetics (2017, methodology): “(13) and (14)) confirm that the kinetic model learned by the VAMPnet successfully predicts the long-time kinetics (Fig.”
Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning (2019, methodology): “To promote simultaneous sampling of high- and low-probability configurations, we included an RC loss using two slow collective coordinates that have been used earlier in the analysis of this BPTI simulation (supplementary materials) (29, 30).”