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Workshop: Generative AI and Biology (GenBio@NeurIPS2023)
Transition Path Sampling with Boltzmann Generator-based MCMC Moves
Michael Plainer · Hannes Stärk · Charlotte Bunne · Stephan Günnemann
Keywords: [ MCMC ] [ Boltzmann generator ] [ Transition Path Sampling ] [ normalizing flow ]
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.