GraphNVP: An Invertible Flow Model for Generating Molecular Graphs

Understand

We propose GraphNVP, the first invertible, normalizing flow-based molecular graph generation model.

• We decompose the generation of a graph into two steps: generation of (i) an adjacency tensor and (ii) node attributes.
• This decomposition yields the exact likelihood maximization on graph-structured data, combined with two novel reversible flows.
• We empirically demonstrate that our model efficiently generates valid molecular graphs with almost no duplicated molecules.