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

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 logvariance 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. We provide links to our project page and code.

Recently, machine learning has emerged as a promising paradigm for CV-free transition path sampling (TPS) (Das et al., 2021;Lelièvre et al., 2023;Holdijk et al., 2024). The key idea is to parameterize the bias force using a neural network and train it to sample transition paths directly via the biased MD simulation. In particular, Lelièvre et al. (2023) considered reinforcement learning to sample paths