@misc{mardt2022deep,
author = {Mardt, Andreas and Hempel, Tim and Clementi, Cecilia and Noé, Frank},
title = {Deep learning to decompose macromolecules into independent Markovian domains},
howpublished = {bioarXiv preprint},
year = {2022},
month = {March},
abstract = {The increasing interest in modeling the dynamics of ever larger proteins has revealed a fundamental problem with models that describe the molecular system as being in a global configuration state. This notion limits our ability to gather sufficient statistics of state probabilities or state-to-state transitions because for large molecular systems the number of metastable states grows exponentially with size. In this manuscript, we approach this challenge by introducing a method that combines our recent progress on independent Markov decomposition (IMD) with VAMPnets, a deep learning approach to Markov modeling. We establish a training objective that quantifies how well a given decomposition of the molecular system into independent subdomains with Markovian dynamics approximates the overall dynamics. By constructing an end-to-end learning framework, the decomposition into such subdomains and their individual Markov state models are simultaneously learned, providing a dataefficient and easily interpretable summary of the complex system dynamics. While learning the dynamical coupling between Markovian subdomains is still an open issue, the present results are a significant step towards learning “Ising models” of large molecular complexes from simulation data.},
url = {http://approjects.co.za/?big=en-us/research/publication/deep-learning-to-decompose-macromolecules-into-independent-markovian-domains/},
}