  Discovering new drug molecules is a pivotal yet challenging process due to
the near-infinitely large chemical space and notorious demands on time and
resources. Numerous generative models have recently been introduced to
accelerate the drug discovery process, but their progression to experimental
validation remains limited, largely due to a lack of consideration for
synthetic accessibility in practical settings. In this work, we introduce a
novel framework that is capable of generating new chemical structures while
ensuring synthetic accessibility. Specifically, we introduce a postfix notation
of synthetic pathways to represent molecules in chemical space. Then, we design
a transformer-based model to translate molecular graphs into postfix notations
of synthesis. We highlight the model's ability to: (a) perform bottom-up
synthesis planning more accurately, (b) generate structurally similar,
synthesizable analogs for unsynthesizable molecules proposed by generative
models with their properties preserved, and (c) explore the local synthesizable
chemical space around hit molecules.
