  There is an increasing demand for computing the relevant structures,
equilibria and long-timescale kinetics of biomolecular processes, such as
protein-drug binding, from high-throughput molecular dynamics simulations.
Current methods employ transformation of simulated coordinates into structural
features, dimension reduction, clustering the dimension-reduced data, and
estimation of a Markov state model or related model of the interconversion
rates between molecular structures. This handcrafted approach demands a
substantial amount of modeling expertise, as poor decisions at any step will
lead to large modeling errors. Here we employ the variational approach for
Markov processes (VAMP) to develop a deep learning framework for molecular
kinetics using neural networks, dubbed VAMPnets. A VAMPnet encodes the entire
mapping from molecular coordinates to Markov states, thus combining the whole
data processing pipeline in a single end-to-end framework. Our method performs
equally or better than state-of-the art Markov modeling methods and provides
easily interpretable few-state kinetic models.
