MolGrow: A Graph Normalizing Flow for Hierarchical Molecular Generation

Authors

  • Maksim Kuznetsov Insilico Medicine
  • Daniil Polykovskiy Insilico Medicine

Keywords:

Neural Generative Models & Autoencoders, Graph-based Machine Learning

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 first layer cause global structural changes, while perturbations in the consequent layers change the resulting molecule only marginally. 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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Published

2021-05-18

How to Cite

Kuznetsov, M., & Polykovskiy, D. (2021). MolGrow: A Graph Normalizing Flow for Hierarchical Molecular Generation. Proceedings of the AAAI Conference on Artificial Intelligence, 35(9), 8226-8234. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17001

Issue

Section

AAAI Technical Track on Machine Learning II