Scaling Deep Learning Solutions for Transition Path Sampling

Jungyoon Lee, Michael Plainer, Yuanqi Du, Lars Holdijk, Rob Brekelmans, Carla P Gomes, Dominique Beaini, Kirill Neklyudov

Track: Machine learning: computational method and/or computational results

Nature Biotechnology: Yes

Keywords: generative model, sampling, transition path sampling, rare event

Abstract:

Transition path sampling (TPS) is an important method for studying rare events, such as they happen in chemical reactions or protein folding. These events occur so infrequently that traditional simulations are often impractical, and even recent machine-learning approaches struggle to address this issue for larger systems. In this paper, we propose using modern deep learning techniques to improve the scalability of TPS methods significantly. We highlight the need for better evaluations in the existing literature and start by formulating TPS as a sampling problem over an unnormalized target density and introduce relevant evaluation metrics to assess the effectiveness of TPS solutions from this perspective. To develop a scalable approach, we explore several design choices, including a problem-informed neural network architecture, simulated annealing, the integration of prior knowledge into the sampling process, and attention mechanisms. Finally, we conduct a comprehensive empirical study and compare these design choices with other recently developed deep-learning methods for rare event sampling.

Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.

Presenter: Jungyoon Lee

Format: Maybe: the presenting author will attend in person, contingent on other factors that still need to be determined (e.g., visa, funding).

Funding: Yes, the presenting author of this submission falls under ICLR’s funding aims, and funding would significantly impact their ability to attend the workshop in person.

Submission Number: 55