Machine Learning Predicts Degree of Aromaticity from Structural Fingerprints

Prediction of whether a compound
is “aromatic” is
at first glance a relatively simple taskdoes it obey Hückel’s
rule (planar cyclic π-system with 4n + 2 electrons) or not?
However, aromaticity is far from a binary property, and there are
distinct variations in the chemical and biological behavior of different
systems which obey Hückel’s rule and are thus classified
as aromatic. To that end, the aromaticity of each molecule in a large
public dataset was quantified by an extension of the work of Raczyńska
et al. Building on this data, a method is proposed for machine learning
the degree of aromaticity of each aromatic ring in a molecule. Categories
are derived from the numeric results, allowing the differentiation
of structural patterns between them and thus a better representation
of the underlying chemical and biological behavior in expert and (Q)­SAR
systems.