Graph Residual Flow for Molecular Graph Generation

TL;DR

This paper introduces Graph Residual Flow (GRF), an invertible flow model for molecular graph generation that offers flexible mappings, theoretical invertibility conditions, and comparable performance with fewer parameters.

Contribution

The paper proposes a novel invertible flow model for molecular graphs, leveraging residual flows for enhanced flexibility and theoretical invertibility conditions.

Findings

Achieves comparable generation performance to existing models.

Uses significantly fewer trainable parameters.

Maintains invertibility throughout training and sampling.

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 achieves comparable generation performance, with much smaller number of trainable parameters compared to the existing flow-based model.