  Recent advancements in deep learning-based modeling of molecules promise to
accelerate in silico drug discovery. A plethora of generative models is
available, building molecules either atom-by-atom and bond-by-bond or
fragment-by-fragment. However, many drug discovery projects require a fixed
scaffold to be present in the generated molecule, and incorporating that
constraint has only recently been explored. Here, we propose MoLeR, a
graph-based model that naturally supports scaffolds as initial seed of the
generative procedure, which is possible because it is not conditioned on the
generation history. Our experiments show that MoLeR performs comparably to
state-of-the-art methods on unconstrained molecular optimization tasks, and
outperforms them on scaffold-based tasks, while being an order of magnitude
faster to train and sample from than existing approaches. Furthermore, we show
the influence of a number of seemingly minor design choices on the overall
performance.
