Graph Residual Flow for Molecular Graph Generation

Shion Honda, Hirotaka Akita, Katsuhiko Ishiguro, Toshiki Nakanishi, Kenta Oono

Keywords: deep generative model, normalizing flow, graph generation, cheminformatics

TL;DR: We propose a residual flow model for molecular graphs, derive the conditions so that the flow is invertible, and show its efficacy in experiments.

Abstract: Statistical generative models for molecular graphs attract attention from many researchers from the fields of bio- and chemo-informatics. Among these models, invertible flow-based approaches are not fully explored yet. In this paper, we propose a powerful invertible flow for molecular graphs, called Graph Residual Flow (GRF). The GRF is based on residual flows, which are known for more flexible and complex non-linear mappings than traditional coupling flows. We theoretically derive non-trivial conditions such that GRF is invertible, and present a way of keeping the entire flows invertible throughout the training and sampling. Experimental results show that a generative model based on the proposed GRF achieve comparable generation performance, with much smaller number of trainable parameters compared to the existing flow-based model.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:1909.13521/code)