Dimensionality reduction methods for molecular simulations

10/29/2017
by   Stefan Doerr, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/27/2021

Variational embedding of protein folding simulations using gaussian mixture variational autoencoders

Conformational sampling of biomolecules using molecular dynamics simulat...
research
10/02/2022

Benign Autoencoders

The success of modern machine learning algorithms depends crucially on e...
research
12/02/2019

Spatially and Temporally Coherent Visual Summaries

When exploring large time-varying data sets, visual summaries are a usef...
research
10/30/2022

Gravitational Dimensionality Reduction Using Newtonian Gravity and Einstein's General Relativity

Due to the effectiveness of using machine learning in physics, it has be...
research
10/27/2018

Monitoring the shape of weather, soundscapes, and dynamical systems: a new statistic for dimension-driven data analysis on large data sets

Dimensionality-reduction methods are a fundamental tool in the analysis ...
research
01/11/2023

Fast conformational clustering of extensive molecular dynamics simulation data

We present an unsupervised data processing workflow that is specifically...
research
10/16/2017

VAMPnets: Deep learning of molecular kinetics

Here we develop a deep learning framework for molecular kinetics from mo...

Please sign up or login with your details

Forgot password? Click here to reset