MolGrow: A Graph Normalizing Flow for Hierarchical Molecular Generation

• URL: http://arxiv.org/abs/2106.05856v1
• Date: Wed, 3 Feb 2021 17:48:52 GMT
• Title: MolGrow: A Graph Normalizing Flow for Hierarchical Molecular Generation
• Authors: Maksim Kuznetsov, Daniil Polykovskiy
• Abstract summary: We propose a hierarchical normalizing flow model for generating molecular graphs. The model produces new molecular structures from a single-node graph by splitting every node into two. We show successful experiments on global and constrained optimization of chemical properties using latent codes of the model.
• Score: 9.594432031144716
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a hierarchical normalizing flow model for generating molecular graphs. The model produces new molecular structures from a single-node graph by recursively splitting every node into two. All operations are invertible and can be used as plug-and-play modules. The hierarchical nature of the latent codes allows for precise changes in the resulting graph: perturbations in the top layer cause global structural changes, while perturbations in the consequent layers change the resulting molecule marginally. The proposed model outperforms existing generative graph models on the distribution learning task. We also show successful experiments on global and constrained optimization of chemical properties using latent codes of the model.

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