NeuralTSNE: A Python Package for the Dimensionality Reduction of Molecular Dynamics Data Using Neural Networks

TL;DR

NeuralTSNE is a Python package that implements a neural network-based parametric t-SNE for effective dimensionality reduction of high-dimensional molecular dynamics data, facilitating better analysis of complex trajectories.

Contribution

The paper introduces NeuralTSNE, a user-friendly Python package utilizing neural networks for parametric t-SNE, optimized for analyzing molecular dynamics data.

Findings

NeuralTSNE demonstrates superior performance over standard t-SNE.

The package is easy to integrate with PyTorch and PyTorch Lightning.

It effectively reduces dimensionality of MD data for insightful analysis.

Abstract

Unsupervised machine learning has recently gained much attention in the field of molecular dynamics (MD). Particularly, dimensionality reduction techniques have been regularly employed to analyze large volumes of high-dimensional MD data to gain insight into hidden information encoded in MD trajectories. Among many such techniques, t-distributed stochastic neighbor embedding (t-SNE) is particularly popular. A parametric version of t-SNE that employs neural networks is less commonly known, yet it has demonstrated superior performance in dimensionality reduction compared to the standard implementation. Here, we present a Python package called NeuralTSNE with our implementation of parametric t-SNE. The implementation is done using the PyTorch library and the PyTorch Lightning framework and can be imported as a module or used from the command line. We show that NeuralTSNE offers an easy-to-use tool for the analysis of MD data.