“Ring Breaker”: Neural Network Driven Synthesis Prediction of the Ring System Chemical Space

Ring
systems in pharmaceuticals, agrochemicals, and dyes are ubiquitous
chemical motifs. While the synthesis of common ring systems is well
described and novel ring systems can be readily and computationally
enumerated, the synthetic accessibility of unprecedented ring systems
remains a challenge. “Ring Breaker” uses a data-driven
approach to enable the prediction of ring-forming reactions, for which
we have demonstrated its utility on frequently found and unprecedented
ring systems, in agreement with literature syntheses. We demonstrate
the performance of the neural network on a range of ring fragments
from the ZINC and DrugBank databases and highlight its potential for
incorporation into computer aided synthesis planning tools. These
approaches to ring formation and retrosynthetic disconnection offer
opportunities for chemists to explore and select more efficient syntheses/synthetic
routes.