  Finding low dimensional representation of data from long-timescale
trajectories of biomolecular processes such as protein-folding or
ligand-receptor binding is of fundamental importance and kinetic models such as
Markov modeling have proven useful in describing the kinetics of these systems.
Recently, an unsupervised machine learning technique called VAMPNet was
introduced to learn the low dimensional representation and linear dynamical
model in an end-to-end manner. VAMPNet is based on variational approach to
Markov processes (VAMP) and relies on neural networks to learn the
coarse-grained dynamics. In this contribution, we combine VAMPNet and graph
neural networks to generate an end-to-end framework to efficiently learn
high-level dynamics and metastable states from the long-timescale molecular
dynamics trajectories. This method bears the advantages of graph representation
learning and uses graph message passing operations to generate an embedding for
each datapoint which is used in the VAMPNet to generate a coarse-grained
representation. This type of molecular representation results in a higher
resolution and more interpretable Markov model than the standard VAMPNet
enabling a more detailed kinetic study of the biomolecular processes. Our
GraphVAMPNet approach is also enhanced with an attention mechanism to find the
important residues for classification into different metastable states.
