  We consider the problem of molecular graph generation using deep models.
While graphs are discrete, most existing methods use continuous latent
variables, resulting in inaccurate modeling of discrete graph structures. In
this work, we propose GraphDF, a novel discrete latent variable model for
molecular graph generation based on normalizing flow methods. GraphDF uses
invertible modulo shift transforms to map discrete latent variables to graph
nodes and edges. We show that the use of discrete latent variables reduces
computational costs and eliminates the negative effect of dequantization.
Comprehensive experimental results show that GraphDF outperforms prior methods
on random generation, property optimization, and constrained optimization
tasks.
