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

Understanding transition pathways between meta-stable states in molecular systems is crucial to advance material design and drug discovery. However, unbiased molecular dynamics simulations are computationally infeasible due to the high energy barriers separating these states. Although recent machine learning techniques offer potential solutions, they are often limited to simple systems or rely on collective variables (CVs) derived from costly domain expertise. In this paper, we introduce a novel approach that trains diffusion path samplers (DPS) for transition path sampling (TPS) without the need for CVs. We recast the problem as an amortized sampling of the target path measure, minimizing the log-variance divergence between the path measure induced by our DPS and the target path measure. To ensure scalability for high-dimensional tasks, we introduce (1) a new off-policy training objective based on learning control variates with replay buffers and (2) a scale-based equivariant parameterization of the bias forces. We evaluate our approach, coined TPS-DPS, on a synthetic double-well potential and three peptides: Alanine Dipeptide, Polyproline Helix, and Chignolin. Results show that our approach produces more realistic and diverse transition pathways compared to existing baselines.