A Hybrid Bottom-Up and Data-Driven Machine Learning Approach for Accurate Coarse-Graining of Large Molecular Complexes.

ABSTRACT: Bottom-up coarse-graining refers to the development of low-resolution simulation models that are thermodynamically consistent with certain distributions from fully atomistic simulations. Force-matching and relative entropy minimization represent two major, frequently applied methods that allow to develop such bottom-up models. Nevertheless, atomistic simulations can often provide only limited sampling of the phase space. For bottom-up coarse-graining, these limitations may result in overfitting of the atomistic reference data, especially for large molecular complexes, where the learning may be agnostic of the actual affinities between binding partners. As a solution to this problem, we devise a data-driven machine learning hybrid coarse-graining concept that represents a regularized versio

SUBMITTER: Liebl K 

PROVIDER: S-EPMC12268871 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature