Tiered graph autoencoders provide the architecture and mechanisms for learning tiered latent representations and latent spaces for molecular graphs that explicitly represent and utilize groups (e.g., functional groups). This enables the utilization and exploration of tiered molecular latent spaces, either individually - the node (atom) tier, the group tier, or the graph (molecule) tier - or jointly, as well as navigation across the tiers. In this paper, we discuss the use of tiered graph autoencoders together with graph prediction for molecular graphs. We show features of molecular graphs used, and groups in molecular graphs identified for some sample molecules. We briefly review graph prediction and the QM9 dataset for background information, and discuss the use of tiered graph embeddings for graph prediction, particularly weighted group pooling. We find that functional groups and ring groups effectively capture and represent the chemical essence of molecular graphs (structures). Further, tiered graph autoencoders and graph prediction together provide effective, efficient and interpretable deep learning for molecular graphs, with the former providing unsupervised, transferable learning and the latter providing supervised, task-optimized learning.

翻译：磁形图形自动编码器提供结构和机制,用于学习明确代表和利用各组(例如功能组)的分子图的分层潜在表层和潜在空间,从而能够利用和探索分层分子潜层空间,无论是单个的节点(原子)层、组级或图(分子库)层,还是联合的,以及跨层级的导航。在本文件中,我们讨论使用分层图形自动图以及分子图的图预测。我们展示了所使用的分子图的特征,以及某些样本分子分子图中查明的分子图组。我们简要审查了图表预测和QM9数据集的背景信息,并讨论了图表预测中使用的分层图形嵌入,特别是加权群集。我们发现,功能组和环组有效捕捉和代表分子图(结构)的化学精髓。此外,分层图形自动组和图形预测一起为分子图提供了有效、高效和可解释的深层学习,前一个提供未超超、可转移和受监督的任务。