Transition Path Sampling with Improved Off-Policy Training of Diffusion Path Samplers

TL;DR

This paper introduces TPS-DPS, a novel diffusion path sampler that efficiently learns transition pathways in molecular systems without relying on domain-specific collective variables, improving diversity and realism of sampled paths.

Contribution

The paper presents a new off-policy training method for diffusion path samplers that eliminates the need for collective variables and enhances sampling efficiency and diversity.

Findings

Produces more realistic transition pathways than existing methods.

Effective in small peptides and fast-folding proteins.

Improves sample diversity and efficiency.

Abstract

Understanding transition pathways between two meta-stable states of a molecular system is crucial to advance drug discovery and material design. However, unbiased molecular dynamics (MD) simulations are computationally infeasible because of the high energy barriers that separate these states. Although recent machine learning techniques are proposed to sample rare events, they are often limited to simple systems and rely on collective variables (CVs) derived from costly domain expertise. In this paper, we introduce a novel approach that trains diffusion path samplers (DPS) to address the transition path sampling (TPS) problem without requiring CVs. We reformulate the problem as an amortized sampling from the transition path distribution by minimizing the log-variance divergence between the path distribution induced by DPS and the transition path distribution. Based on the log-variance divergence, we propose learnable control variates to reduce the variance of gradient estimators and the off-policy training objective with replay buffers and simulated annealing techniques to improve sample efficiency and diversity. We also propose a scale-based equivariant parameterization of the bias forces to ensure scalability for large systems. We extensively evaluate our approach, termed TPS-DPS, on a synthetic system, small peptide, and challenging fast-folding proteins, demonstrating that it produces more realistic and diverse transition pathways than existing baselines.