  Retrosynthesis analysis is a critical task in organic chemistry central to
many important industries. Previously, various machine learning approaches have
achieved promising results on this task by representing output molecules as
strings and autoregressively decoded token-by-token with generative models.
Text generation or machine translation models in natural language processing
were frequently utilized approaches. The token-by-token decoding approach is
not intuitive from a chemistry perspective because some substructures are
relatively stable and remain unchanged during reactions. In this paper, we
propose a substructure-level decoding model, where the substructures are
reaction-aware and can be automatically extracted with a fully data-driven
approach. Our approach achieved improvement over previously reported models,
and we find that the performance can be further boosted if the accuracy of
substructure extraction is improved. The substructures extracted by our
approach can provide users with better insights for decision-making compared to
existing methods. We hope this work will generate interest in this fast growing
and highly interdisciplinary area on retrosynthesis prediction and other
related topics.
