MolGrow: A Graph Normalizing Flow for Hierarchical Molecular Generation
This work addresses the challenge of generating novel molecular structures with precise control, which is important for drug discovery and materials science.
This paper introduces a hierarchical normalizing flow model, MolGrow, for generating molecular graphs by recursively splitting nodes. The model achieves superior performance on distribution learning compared to existing generative graph models and demonstrates successful application in global and constrained optimization of chemical properties.
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.