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arXiv · 2106.15098

Molecule Generation by Principal Subgraph Mining and Assembling

Xiangzhe Kong·Wenbing Huang·Zhixing Tan·Yang Liu
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Abstract

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.

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Xiangzhe Kong, Wenbing Huang, Zhixing Tan, Yang Liu. 2021-06-29. Molecule Generation by Principal Subgraph Mining and Assembling. https://arxiv.org/abs/2106.15098

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