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

As deep Variational Auto-Encoder (V AE) frameworks become more widely used for modeling biomolecular simulation data, we emphasize the capability of the V AE 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. A loss function is used to train both networks concurrently. In the process of developing auto-encoder-based models for simulation data, several modifications to the original V AE loss function have been proposed to better suit the analysis of time-series data, and a more thorough analysis of the effect of these modifications on the modelled latent space is needed.