SCScore: Synthetic Complexity Learned from a Reaction Corpus

Several definitions
of molecular complexity exist to facilitate
prioritization of lead compounds, to identify diversity-inducing and
complexifying reactions, and to guide retrosynthetic searches. In
this work, we focus on synthetic complexity and reformalize its definition
to correlate with the expected number of reaction steps required to
produce a target molecule, with implicit knowledge about what compounds
are reasonable starting materials. We train a neural network model
on 12 million reactions from the Reaxys database to impose a pairwise
inequality constraint enforcing the premise of this definition: that
on average, the products of published chemical reactions should be
more synthetically complex than their corresponding reactants. The
learned metric (SCScore) exhibits highly desirable nonlinear behavior,
particularly in recognizing increases in synthetic complexity throughout
a number of linear synthetic routes.