# Algorithmic dimensionality reduction for molecular structure analysis.

@article{Brown2008AlgorithmicDR, title={Algorithmic dimensionality reduction for molecular structure analysis.}, author={W. Michael Brown and Shawn Martin and Sara N. Pollock and Evangelos A. Coutsias and Jean-Paul Watson}, journal={The Journal of chemical physics}, year={2008}, volume={129 6}, pages={ 064118 } }

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…

## 73 Citations

### Using Dimensionality Reduction to Analyze Protein Trajectories

- Computer ScienceFront. Mol. Biosci.
- 2019

This paper analyzed a molecular dynamics trajectory of the C-terminal fragment of the immunoglobulin binding domain B1 of protein G of Streptococcus modeled in explicit solvent using a range of different dimensionality reduction algorithms and tried to systematically compare the projections generated using each of these algorithms by using a clustering algorithm to find the positions and extents of the basins in the high-dimensional energy landscape.

### Geometric Issues in Dimensionality Reduction and Protein Conformation Space

- Computer Science
- 2014

The puzzling dimensionally reduction results of β-hairpin are discussed where the linear method PCA performed better than nonlinear methods ISOMAP and LLE and it is shown that nonlinear surfaces without certain specified properties are not necessarily better suited for nonlinear dimensionality reduction methods than linear methods.

### Metadynamics in the conformational space nonlinearly dimensionally reduced by Isomap.

- ChemistryThe Journal of chemical physics
- 2011

A simulation with a bias potential acting in the directions of collective motions determined by a nonlinear dimensionality reduction method is presented, which allows to use essentially any parameter of the system as a collective variable in biased simulations.

### UMAP as a Dimensionality Reduction Tool for Molecular Dynamics Simulations of Biomacromolecules: A Comparison Study.

- Computer ScienceThe journal of physical chemistry. B
- 2021

The comparison of the raw high-dimensional data with the projections obtained using different dimensionality reduction methods based on various metrics showed that UMAP has superior performance when compared with linear reduction methods (PCA and tICA) and has competitive performance and scalable computational cost.

### Evaluation of Dimensionality-reduction Methods from Peptide Folding-unfolding Simulations.

- Computer ScienceJournal of chemical theory and computation
- 2013

This study evaluated several non linear methods, locally linear embedding, Isomap, and diffusion maps, as well as principal component analysis from the equilibrium folding/unfolding trajectory of the second β-hairpin of the B1 domain of streptococcal protein G.

### Machine learning in multiscale modeling and simulations of molecular systems

- Computer Science
- 2015

A novel method is proposed, atlas of collective variables, that systematically overcomes topological obstacles, ameliorates the geometrical distortions and thus allows NLDR techniques to perform optimally in molecular simulations.

### Unsupervised Learning Methods for Molecular Simulation Data

- Computer ScienceChemical reviews
- 2021

This Review provides a comprehensive overview of the methods of unsupervised learning that have been most commonly used to investigate simulation data and indicates likely directions for further developments in the field.

### Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions.

- Computer ScienceThe Journal of chemical physics
- 2014

It is demonstrated how manifold learning techniques may complement and enhance informed intuition commonly used to construct reduced descriptions of the dynamics in molecular conformation space to construct robust Markov state models.

### Constructing Grids for Molecular Quantum Dynamics Using an Autoencoder.

- Computer ScienceJournal of chemical theory and computation
- 2018

A machine learning approach is presented that utilizes an autoencoder that is trained to find a low-dimensional representation of a set of molecular configurations that can be used to generate a potential energy surface grid in the desired subspace.

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