Variational Encoding of Complex Dynamics
This provides a method for more accurately modeling and interpreting complex biophysics, though it is incremental as it builds on existing variational autoencoder techniques.
The authors tackled the problem of interpreting high-dimensional time-series data in chemical and biophysical systems by developing a time-lagged variational autoencoder (VDE) to compress nonlinear dynamics into a single embedding, demonstrating high fidelity in examples like Brownian dynamics and protein folding.
Often the analysis of time-dependent chemical and biophysical systems produces high-dimensional time-series data for which it can be difficult to interpret which individual features are most salient. While recent work from our group and others has demonstrated the utility of time-lagged co-variate models to study such systems, linearity assumptions can limit the compression of inherently nonlinear dynamics into just a few characteristic components. Recent work in the field of deep learning has led to the development of variational autoencoders (VAE), which are able to compress complex datasets into simpler manifolds. We present the use of a time-lagged VAE, or variational dynamics encoder (VDE), to reduce complex, nonlinear processes to a single embedding with high fidelity to the underlying dynamics. We demonstrate how the VDE is able to capture nontrivial dynamics in a variety of examples, including Brownian dynamics and atomistic protein folding. Additionally, we demonstrate a method for analyzing the VDE model, inspired by saliency mapping, to determine what features are selected by the VDE model to describe dynamics. The VDE presents an important step in applying techniques from deep learning to more accurately model and interpret complex biophysics.