A Predictive Tool for Electrophilic Aromatic Substitutions Using Machine Learning

At
the early stages of the drug development process, thousands
of compounds are synthesized in order to attain the best possible
potency and pharmacokinetic properties. Once successful scaffolds
are identified, large libraries of analogues are made, which is a
challenging and time-consuming task. Recently, late stage functionalization
(LSF) has become increasingly prominent since these reactions selectively
functionalize C–H bonds, allowing to quickly produce analogues.
Classical electrophilic aromatic halogenations are a powerful type
of reaction in the LSF toolkit. However, the introduction of
an electrophile in a regioselective manner on a drug-like molecule
is a challenging task. Herein we present a machine learning model
able to predict the reactive site of an electrophilic aromatic substitution
with an accuracy of 93% (internal validation set). The model takes
as input a SMILES of a compound and uses six quantum mechanics descriptors
to identify its reactive site(s). On an external validation set, 90%
of all molecules were correctly predicted.