This work presents efforts to augment the performance of data-driven machine learning
algorithms for reaction template recommendation used in computer-aided synthesis
planning software. Often, machine learning models designed to perform the task of
prioritizing reaction templates or molecular transformations are focused on reporting high-
accuracy metrics for the one-to-one mapping of product molecules in reaction databases to
the template extracted from the recorded reaction. The available templates that get selected …

A key step in computer-aided synthesis planning (CASP) is the prioritization of candidate
molecular transformations for retrosynthetic analysis. Recent methods obtaining state-of-the-
art accuracy have used machine learning (ML) models as recommendation engines to rank
reaction templates extracted from databases of recorded reactions. However, data scarcity
limits the ability for ML models to recommend rare, often highly desired, transformations. In
this work we discuss the augmentation of open-access reaction databases with synthetically …