  An effective implementation of enhanced sampling algorithms for molecular
dynamics simulations requires a priori knowledge of the approximate reaction
coordinate describing the relevant mechanisms in the system. Here we
demonstrate how the artificial intelligence based recent State Predictive
Information Bottleneck (SPIB) approach can learn such a reaction coordinate as
a deep neural network even from under-sampled trajectories. We demonstrate its
usefulness by achieving more than 40 magnitudes of acceleration in simulating
two test-piece biophysical systems through well-tempered metadynamics performed
by biasing along the SPIB learned reaction coordinate. These include left- to
right- handed chirality transitions in a synthetic protein (Aib)_9, and
permeation of a small, asymmetric molecule benzoic acid through a synthetic,
symmetric phospholipid bilayer. In addition to significantly accelerating the
dynamics and achieving back-and-forth movement between different metastable
states, the SPIB based reaction coordinate gives mechanistic insight into the
processes driving these two important problems.
