  Identifying molecules that exhibit some pre-specified properties is a
difficult problem to solve. In the last few years, deep generative models have
been used for molecule generation. Deep Graph Variational Autoencoders are
among the most powerful machine learning tools with which it is possible to
address this problem. However, existing methods struggle in capturing the true
data distribution and tend to be computationally expensive. In this work, we
propose RGCVAE, an efficient and effective Graph Variational Autoencoder based
on: (i) an encoding network exploiting a new powerful Relational Graph
Isomorphism Network; (ii) a novel probabilistic decoding component. Compared to
several state-of-the-art VAE methods on two widely adopted datasets, RGCVAE
shows state-of-the-art molecule generation performance while being
significantly faster to train.
