Transition Path Sampling with Boltzmann Generator-based MCMC Moves

2023/12/08 by Michael Plainer, Plainer, Michael, H. Stärk +5 · 1 citation
Biochemistry, Genetics and Molecular Biology · Chemistry · Materials Science · #Protein Structure and Dynamics #Mass Spectrometry Techniques and Applications #Machine Learning in Materials Science

paper · pdf · doi:10.48550/arxiv.2312.05340

Abstract

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.

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