Note: Variational Encoding of Protein Dynamics Benefits from Maximizing Latent Autocorrelation

TL;DR

This paper demonstrates that maximizing latent autocorrelation in Variational Auto-Encoders improves the modeling of slow biomolecular processes by optimizing the timescale of the latent space.

Contribution

It introduces a method that leverages latent autocorrelation in VAE training to better capture slow conformational dynamics in biomolecular simulations.

Findings

VAE frameworks can maximize latent space timescales.

Autocorrelation loss improves slow process representation.

VDE framework yields variationally optimized latent coordinates.

Abstract

As deep Variational Auto-Encoder (VAE) frameworks become more widely used for modeling biomolecular simulation data, we emphasize the capability of the VAE architecture to concurrently maximize the timescale of the latent space while inferring a reduced coordinate, which assists in finding slow processes as according to the variational approach to conformational dynamics. We additionally provide evidence that the VDE framework (Hern\'andez et al., 2017), which uses this autocorrelation loss along with a time-lagged reconstruction loss, obtains a variationally optimized latent coordinate in comparison with related loss functions. We thus recommend leveraging the autocorrelation of the latent space while training neural network models of biomolecular simulation data to better represent slow processes.