  The limits of molecular dynamics (MD) simulations of macromolecules are
steadily pushed forward by the relentless developments of computer
architectures and algorithms. This explosion in the number and extent (in size
and time) of MD trajectories induces the need of automated and transferable
methods to rationalise the raw data and make quantitative sense out of them.
Recently, an algorithmic approach was developed by some of us to identify the
subset of a protein's atoms, or mapping, that enables the most informative
description of it. This method relies on the computation, for a given reduced
representation, of the associated mapping entropy, that is, a measure of the
information loss due to the simplification. Albeit relatively straightforward,
this calculation can be time consuming. Here, we describe the implementation of
a deep learning approach aimed at accelerating the calculation of the mapping
entropy. The method relies on deep graph networks, which provide extreme
flexibility in the input format. We show that deep graph networks are accurate
and remarkably efficient, with a speedup factor as large as $10^5$ with respect
to the algorithmic computation of the mapping entropy. Applications of this
method, which entails a great potential in the study of biomolecules when used
to reconstruct its mapping entropy landscape, reach much farther than this,
being the scheme easily transferable to the computation of arbitrary functions
of a molecule's structure.
