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arXiv · 1909.13521

Graph Residual Flow for Molecular Graph Generation

Shion Honda·Hirotaka Akita·Katsuhiko Ishiguro·Toshiki Nakanishi·Kenta Oono
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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.

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Shion Honda, Hirotaka Akita, Katsuhiko Ishiguro, Toshiki Nakanishi, Kenta Oono. 2019-09-30. Graph Residual Flow for Molecular Graph Generation. https://arxiv.org/abs/1909.13521

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