Models Matter: The Impact of Single-Step Retrosynthesis on Synthesis
  Planning
Paula Torren-Peraire Alan Kai Hassen Samuel Genheden Jonas Verhoeven Djork-Arne Clevert Mike Preuss Igor Tetko
Retrosynthesis consists of breaking down a chemical compound recursively
step-by-step into molecular precursors until a set of commercially available
molecules is found with the goal to provide a synthesis route. Its two primary
research directions, single-step retrosynthesis prediction, which models the
chemical reaction logic, and multi-step synthesis planning, which tries to find
the correct sequence of reactions, are inherently intertwined. Still, this
connection is not reflected in contemporary research. In this work, we combine
these two major research directions by applying multiple single-step
retrosynthesis models within multi-step synthesis planning and analyzing their
impact using public and proprietary reaction data. We find a disconnection
between high single-step performance and potential route-finding success,
suggesting that single-step models must be evaluated within synthesis planning
in the future. Furthermore, we show that the commonly used single-step
retrosynthesis benchmark dataset USPTO-50k is insufficient as this evaluation
task does not represent model performance and scalability on larger and more
diverse datasets. For multi-step synthesis planning, we show that the choice of
the single-step model can improve the overall success rate of synthesis
planning by up to +28% compared to the commonly used baseline model. Finally,
we show that each single-step model finds unique synthesis routes, and differs
in aspects such as route-finding success, the number of found synthesis routes,
and chemical validity, making the combination of single-step retrosynthesis
prediction and multi-step synthesis planning a crucial aspect when developing
future methods.