Deep Dynamic Graphical Models for Molecular Kinetics

Abstract

With the massive growth of molecular dynamics simulation results comes a great demand for efficient analysis methods to distill essential information from simulation and enable quantitative characterization of molecular properties. Dynamic Graphical Model (DGM) is currently the most data-efficient method towards this goal. However, DGMs rely on extensive manual intervention by experts: division of molecules into smaller subsystems and their discretization into an unknown number of states. We aim to automate this expert-guided procedure using a deep-learning approach and make an end-to-end learning system. To achieve this, we examine the Variational Approach to Markov Processes (VAMP), and its ability to detect meta-stable subsystems in molecular systems, and to decide the number of states for each subsystem. We put forward a model which uses VAMP to learn subsystem states via a deep neural network and DGM to connect the subsystems by modeling their time-correlated dynamics. The model is trained in an end-to-end manner and optimized using a weighted sum of VAMP loss, DGM loss, and regularizer. We also introduce a pruning-based algorithm to automatically decide the number of states per subsystem. Our results show that VAMP is suitable for enumerating subsystems of a molecular system, however, VAMP alone cannot decide the number of states for each subsystem. This thesis sheds light on how an end-to-end learning system may be built with DGMs to analyze molecular dynamics and outlines possible future extensions of this work.