Machine Learning Guided Atom Mapping of Metabolic Reactions

Atom
mapping of a chemical reaction is a mapping between the atoms
in the reactant molecules and the atoms in the product molecules.
It encodes the underlying reaction mechanism and, as such, constitutes
essential information in computational studies in drug design. Various
techniques have been investigated for the automatic computation of
the atom mapping of a chemical reaction, approaching the problem as
a graph matching problem. The graph abstraction of the chemical problem,
though, eliminates crucial chemical information. There have been efforts
for enhancing the graph representation by introducing the bond stabilities
as edge weights, as they are estimated based on experimental evidence.
Here, we present a fully automated optimization-based approach, named
AMLGAM (Automated Machine Learning Guided Atom Mapping), that uses
machine learning techniques for the estimation of the bond stabilities
based on the chemical environment of each bond. The optimization method
finds the reaction mechanism which favors the breakage/formation of
the less stable bonds. We evaluated our method on a manually curated
data set of 382 chemical reactions and ran our method on a much larger
and diverse data set of 7400 chemical reactions. We show that the
proposed method improves the accuracy over existing techniques based
on results published by earlier studies on a common data set and is
capable of handling unbalanced reactions.