Memory kernel minimization-based neural networks for discovering slow collective variables of biomolecular dynamics.

ABSTRACT: Identifying collective variables (CVs) that accurately capture the slowest timescales of protein conformational changes is crucial to comprehend numerous biological processes. Here we introduce memory kernel minimization-based neural networks (MEMnets), a deep learning framework that accurately identifies the slow CVs of biomolecular dynamics. Unlike popular CV-identification methods, which typically assume Markovian dynamics, MEMnets is built on the integrative generalized master equation theory, which incorporates non-Markovian dynamics by encoding them in a memory kernel for continuous CVs. The key innovation of MEMnets is the identification of optimal CVs by minimizing the upper bound for the time-integrated memory kernels through parallel encoder networks. We demonstrate that MEMnets

SUBMITTER: Liu B 

PROVIDER: S-EPMC12286716 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature