State Predictive Information Bottleneck

The rapid advances in computational power have made molecular dynamics (MD) a powerful tool for studying biophysical systems. However, there are still at least two open questions in this area: first, how to make use of the deluge of data generated from MD simulation human understandable; second, how to further push the limit of the timescale that can be reached by MD. One key to both difficulties is to uncover a low dimensional manifold (known as reaction coordinate or RC) on which the dynamics of the system can be projected. Here we developed State Predictive Information Bottleneck (SPIB) to learn RC from trajectories. In SPIB, the time delay is interpreted as the minimal time resolution that we care for a dynamic system, and can be used to automatically discretize the high-dimensional state space into a few metastable states. Such a discrete-state representation then can be employed to guide our RC to focus only on the motion related to the state transitions. In this way, the RC learned by SPIB is strongly related to the committor, and is able to identify the correct transition states.