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We propose a hierarchical normalizing flow model for generating moleculargraphs. The model produces new molecular structures from a single-node graph byrecursively splitting every node into two. All operations are invertible andcan be used as plug-and-play modules. The hierarchical nature of the latentcodes allows for precise changes in the resulting graph: perturbations in thetop layer cause global structural changes, while perturbations in theconsequent layers change the resulting molecule marginally. The proposed modeloutperforms existing generative graph models on the distribution learning task.We also show successful experiments on global and constrained optimization ofchemical properties using latent codes of the model.

MolGrow: A Graph Normalizing Flow for Hierarchical Molecular Generation