Graph Residual Flow for Molecular Graph Generation
This work addresses molecular graph generation for researchers in bio- and chemo-informatics, representing an incremental improvement in flow-based methods.
The authors tackled the problem of generating molecular graphs by proposing a new invertible flow-based model called graph residual flow (GRF), which achieved comparable performance to existing models while using significantly fewer parameters.
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.