From the Cover: Simplifying the representation of complex free-energy landscapes using sketch-map.

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Metric Learning and Manifolds: Preserving the Intrinsic Geometry (2017, methodology): “Manifold learning is also used construct low dimensional (cid:16)maps(cid:17) from large classes of molecules, with the ultimate goal of predicting their properties, in e.g (Ceriotti et al., 2011).”
Kinetic distance and kinetic maps from molecular dynamics simulation. (2015, methodology): “Alternative methods to analyze the space explored by MD simulations include sketchmap [9], PCA [1], kernel-PCA [21, 3], and likelihood maximization [19].”
Principles of protein structural ensemble determination. (2017, methodology): “While a detailed discussion about these methods is beyond the scope of this review, we highlight low-dimensionality reduction algorithms such as sketch-map [78], isomap [79,80], and other nonlinear manifold learning algorithms, which offer effective representations of complex free energy landscapes ”
Enhanced Sampling in Molecular Dynamics Using Metadynamics, Replica-Exchange, and Temperature-Acceleration (2013, unknown): “, [169–171]).”