  Machine learning (ML) can be used to construct surrogate models for the fast
prediction of a property of interest. ML can thus be applied to chemical
projects, where the usual experimentation or calculation techniques can take
hours or days for just one sample. In this manner, the most promising candidate
samples could be extracted from an extensive database and subjected to further
in-depth analysis.
  Despite their broad applicability, it can be challenging to apply ML methods
to a given chemical problem since a multitude of design decisions must be made,
such as the molecular descriptor to use or the optimizer to train the model.
  Here we present a methodology for the meaningful exploration of a given
molecular problem through classification experiments. This conceptually simple
methodology results in transferable insight on the selected problem and can be
used as a platform from which prediction difficulty is estimated, molecular
representations are tested and refined, and more precise or ambitious projects
can be undertaken. Physicochemical insight can also be obtained.
  This methodology is illustrated through the use of multiple molecular
descriptors for the prediction of enthalpy, Gibbs' free energy, zero-point
vibrational energy, and constant-volume calorific capacity of the molecules
from the public database QM9 [Ramakrishnan2014] with 133,885 organic molecules.
A noteworthy result is that for the classification problem we propose, the
low-resolution descriptor `atomic composition' [Tchagang2019] can reach a
classification rate almost on par with the high-resolution `sorted Coulomb
matrix' [Rupp2012,Montavon2012,Hansen2013] ($>90\%$), provided that an
appropriate optimizer is used during training.
