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Identifying molecules that exhibit some pre-specified properties is adifficult problem to solve. In the last few years, deep generative models havebeen used for molecule generation. Deep Graph Variational Autoencoders areamong the most powerful machine learning tools with which it is possible toaddress this problem. However, existing methods struggle in capturing the truedata distribution and tend to be computationally expensive. In this work, wepropose RGCVAE, an efficient and effective Graph Variational Autoencoder basedon: (i) an encoding network exploiting a new powerful Relational GraphIsomorphism Network; (ii) a novel probabilistic decoding component. Compared toseveral state-of-the-art VAE methods on two widely adopted datasets, RGCVAEshows state-of-the-art molecule generation performance while beingsignificantly faster to train.

RGCVAE: Relational Graph Conditioned Variational Autoencoder for Molecule Design