With the advancements in deep learning, deep generative models combined with graph neural networks have been successfully employed for data-driven molecular graph generation. Early methods based on the non-autoregressive approach have been effective in generating molecular graphs quickly and efficiently but have suffered from low performance. In this paper, we present an improved learning method involving a graph variational autoencoder for efficient molecular graph generation in a non-autoregressive manner. We introduce three additional learning objectives and incorporate them into the training of the model: approximate graph matching, reinforcement learning, and auxiliary property prediction. We demonstrate the effectiveness of the proposed method by evaluating it for molecular graph generation tasks using QM9 and ZINC datasets. The model generates molecular graphs with high chemical validity and diversity compared with existing non-autoregressive methods. It can also conditionally generate molecular graphs satisfying various target conditions.

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用于分子图生成的非自回归图变分自动编码器的高效学习

随着深度学习的进步，深度生成模型与图神经网络相结合已成功用于数据驱动的分子图生成。基于非自回归方法的早期方法可以有效地快速有效地生成分子图，但性能较低。在本文中，我们提出了一种改进的学习方法，涉及图变分自动编码器，用于以非自回归方式高效生成分子图。我们引入了三个额外的学习目标并将其纳入模型的训练中：近似图匹配、强化学习和辅助属性预测。我们通过使用 QM9 和 ZINC 数据集评估该方法的分子图生成任务来证明该方法的有效性。与现有的非自回归方法相比，该模型生成的分子图具有较高的化学有效性和多样性。它还可以有条件地生成满足各种目标条件的分子图。