Algorithmic dimensionality reduction for molecular structure analysis.

The Journal of Chemical Physics
W Michael BrownJean-Paul Watson

Abstract

Dimensionality reduction approaches have been used to exploit the redundancy in a Cartesian coordinate representation of molecular motion by producing low-dimensional representations of molecular motion. This has been used to help visualize complex energy landscapes, to extend the time scales of simulation, and to improve the efficiency of optimization. Until recently, linear approaches for dimensionality reduction have been employed. Here, we investigate the efficacy of several automated algorithms for nonlinear dimensionality reduction for representation of trans, trans-1,2,4-trifluorocyclo-octane conformation--a molecule whose structure can be described on a 2-manifold in a Cartesian coordinate phase space. We describe an efficient approach for a deterministic enumeration of ring conformations. We demonstrate a drastic improvement in dimensionality reduction with the use of nonlinear methods. We discuss the use of dimensionality reduction algorithms for estimating intrinsic dimensionality and the relationship to the Whitney embedding theorem. Additionally, we investigate the influence of the choice of high-dimensional encoding on the reduction. We show for the case studied that, in terms of reconstruction error root mean squ...Continue Reading

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Citations

Jun 25, 2010·The Journal of Chemical Physics·Shawn MartinJean-Paul Watson
Dec 11, 2013·The Journal of Chemical Physics·Behrooz HashemianMarino Arroyo
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Methods Mentioned

BETA
nuclear
x-ray crystallography
protein folding
NMR
PCA

Software Mentioned

LLE
Intel
OPENMP
Isomap
Math Kernel Library ( MKL
TINKER

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