  The problem of accelerating drug discovery relies heavily on automatic tools
to optimize precursor molecules to afford them with better biochemical
properties. Our work in this paper substantially extends prior state-of-the-art
on graph-to-graph translation methods for molecular optimization. In
particular, we realize coherent multi-resolution representations by
interweaving the encoding of substructure components with the atom-level
encoding of the original molecular graph. Moreover, our graph decoder is fully
autoregressive, and interleaves each step of adding a new substructure with the
process of resolving its attachment to the emerging molecule. We evaluate our
model on multiple molecular optimization tasks and show that our model
significantly outperforms previous state-of-the-art baselines.
