  We have developed an end-to-end, retrosynthesis system, named ChemiRise, that
can propose complete retrosynthesis routes for organic compounds rapidly and
reliably. The system was trained on a processed patent database of over 3
million organic reactions. Experimental reactions were atom-mapped, clustered,
and extracted into reaction templates. We then trained a graph convolutional
neural network-based one-step reaction proposer using template embeddings and
developed a guiding algorithm on the directed acyclic graph (DAG) of chemical
compounds to find the best candidate to explore. The atom-mapping algorithm and
the one-step reaction proposer were benchmarked against previous studies and
showed better results. The final product was demonstrated by retrosynthesis
routes reviewed and rated by human experts, showing satisfying functionality
and a potential productivity boost in real-life use cases.
