Transferring chemical and energetic knowledge between molecular systems with machine learning.

ABSTRACT: Predicting structural and energetic properties of a molecular system is one of the fundamental tasks in molecular simulations, and it has applications in chemistry, biology, and medicine. In the past decade, the advent of machine learning algorithms had an impact on molecular simulations for various tasks, including property prediction of atomistic systems. In this paper, we propose a novel methodology for transferring knowledge obtained from simple molecular systems to a more complex one, endowed with a significantly larger number of atoms and degrees of freedom. In particular, we focus on the classification of high and low free-energy conformations. Our approach relies on utilizing (i) a novel hypergraph representation of molecules, encoding all relevant information for characterizing mu

SUBMITTER: Heydari S 

PROVIDER: S-EPMC9839695 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature