Title: Dimensionality reduction methods for molecular simulations,
Abstract: Molecular simulations produce very high-dimensional data-sets with millions
of data points. As analysis methods are often unable to cope with so many
dimensions, it is common to use dimensionality reduction and clustering methods
to reach a reduced representation of the data. Yet these methods often fail to
capture the most important features necessary for the construction of a Markov
model. Here we demonstrate the results of various dimensionality reduction
methods on two simulation data-sets, one of protein folding and another of
protein-ligand binding. The methods tested include a k-means clustering
variant, a non-linear auto encoder, principal component analysis and tICA. The
dimension-reduced data is then used to estimate the implied timescales of the
slowest process by a Markov state model analysis to assess the quality of the
projection. The projected dimensions learned from the data are visualized to
demonstrate which conformations the various methods choose to represent the
molecular process.