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Retrosynthetic planning aims to devise a complete multi-step synthetic route
from starting materials to a target molecule. Current strategies use a
decoupled approach of single-step retrosynthesis models and search algorithms,
taking only the product as the input to predict the reactants for each planning
step and ignoring valuable context information along the synthetic route. In
this work, we propose a novel framework that utilizes context information for
improved retrosynthetic planning. We view synthetic routes as reaction graphs
and propose to incorporate context through three principled steps: encode
molecules into embeddings, aggregate information over routes, and readout to
predict reactants. Our approach is the first attempt to utilize in-context
reactions for retrosynthetic planning. The entire framework can be efficiently
optimized in an end-to-end fashion and produce more practical and accurate
predictions. Comprehensive experiments demonstrate that by fusing in the
context information over routes, our model significantly improves the
performance of retrosynthetic planning over baselines that are not
context-aware, especially for long synthetic routes. Code is available at
https://github.com/SongtaoLiu0823/FusionRetro.