  Sampling all possible transition paths between two 3D states of a molecular
system has various applications ranging from catalyst design to drug discovery.
Current approaches to sample transition paths use Markov chain Monte Carlo and
rely on time-intensive molecular dynamics simulations to find new paths. Our
approach operates in the latent space of a normalizing flow that maps from the
molecule's Boltzmann distribution to a Gaussian, where we propose new paths
without requiring molecular simulations. Using alanine dipeptide, we explore
Metropolis-Hastings acceptance criteria in the latent space for exact sampling
and investigate different latent proposal mechanisms.
