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.

翻译：识别具有某些预设性质的分子是一个难以求解的问题。近年来，深度生成模型已被用于分子生成。深度图变分自编码器是解决该问题最强大的机器学习工具之一。然而，现有方法在捕捉真实数据分布方面存在困难，且往往计算开销较大。在本工作中，我们提出RGCVAE——一种高效且有效的图变分自编码器，其基于：（i）利用新型强大关系图同构网络的编码网络；（ii）一种新颖的概率解码组件。与两个广泛采用数据集上的多种最先进VAE方法相比，RGCVAE在分子生成性能上达到领先水平，同时训练速度显著更快。