While methods such as PCA might provide some degree of feature-reduction, a major breakthrough in simulation featurization was the use of time-lagged independent component analysis (tICA) , which transforms input coordinates to identify the rarest (which are assumed to be the slowest) motions. For protein folding simulations, this provides excellent dimensionality reduction, since the slowest coordinate is often the rarest and most valuable . However, in simulations of folded proteins the rarest motions observed may be less interesting or artifacts of sampling due to the simulation size. Much success has previously been seen using geometric features, such as cartesian coordinates or dihedral angles . Other features such as solvent accessible surface area (SASA) or ligand-residence-time have also been useful for construction of predictive MSMs . With the advent of deep learning and neural network based methods, a slew of methods have arisen that leverage neural network models to identify key feature spaces to project into.