基于主子图挖掘与组装的分子生成
Molecule Generation by Principal Subgraph Mining and Assembling
摘要：
分子生成是各种应用的核心。目前的注意力已经支付接近子图预测和组装的生成任务。然而，这些方法通常依赖于手工或外部子图构造，并且子图组装仅依赖于局部布置。在本文中，我们定义了一个新的概念，主子图，这是密切相关的分子内的信息模式。有趣的是，我们提出的合并和更新子图提取方法可以自动从数据集中发现频繁主子图，而以前的方法是不能的。此外，我们开发了一个两步子图组装策略，该策略首先以顺序方式预测一组子图，然后将所有生成的子图全局组装为最终输出分子。建立在图变分自动编码器，我们的模型被证明是有效的几个评价指标和效率，与最先进的方法相比，分布学习和（约束）属性优化任务。
英文摘要：
Molecule generation is central to a variety of applications. Current attention has been paid to approaching the generation task as subgraph prediction and assembling. Nevertheless, these methods usually rely on hand-crafted or external subgraph construction, and the subgraph assembling depends solely on local arrangement. In this paper, we define a novel notion, principal subgraph, that is closely related to the informative pattern within molecules. Interestingly, our proposed merge-and-update subgraph extraction method can automatically discover frequent principal subgraphs from the dataset, while previous methods are incapable of. Moreover, we develop a two-step subgraph assembling strategy, which first predicts a set of subgraphs in a sequence-wise manner and then assembles all generated subgraphs globally as the final output molecule. Built upon graph variational auto-encoder, our model is demonstrated to be effective in terms of several evaluation metrics and efficiency, compared with state-of-the-art methods on distribution learning and (constrained) property optimization tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
