  Graph generation techniques are increasingly being adopted for drug
discovery. Previous graph generation approaches have utilized relatively small
molecular building blocks such as atoms or simple cycles, limiting their
effectiveness to smaller molecules. Indeed, as we demonstrate, their
performance degrades significantly for larger molecules. In this paper, we
propose a new hierarchical graph encoder-decoder that employs significantly
larger and more flexible graph motifs as basic building blocks. Our encoder
produces a multi-resolution representation for each molecule in a
fine-to-coarse fashion, from atoms to connected motifs. Each level integrates
the encoding of constituents below with the graph at that level. Our
autoregressive coarse-to-fine decoder adds one motif at a time, interleaving
the decision of selecting a new motif with the process of resolving its
attachments to the emerging molecule. We evaluate our model on multiple
molecule generation tasks, including polymers, and show that our model
significantly outperforms previous state-of-the-art baselines.
