Title: Graph Piece: Efficiently Generating High-Quality Molecular Graphs with Substructures Show Chinese title

Title: 图片段：通过子结构高效生成高质量分子图

Abstract: Molecule generation, which requires generating valid molecules with desired properties, is a fundamental but challenging task. Recent years have witnessed the rapid development of atom-level auto-regressive models, which usually construct graphs following sequential actions of adding atom-level nodes and edges. However, these atom-level models ignore high-frequency substructures, which not only capture the regularities of atomic combination in molecules but are also often related to desired chemical properties, and therefore may be sub-optimal for generating high-quality molecules. In this paper, we propose a method to automatically discover such common substructures, which we call graph pieces, from given molecular graphs. We also present a graph piece variational autoencoder (GP-VAE) for generating molecular graphs based on graph pieces. Experiments show that our GP-VAE models not only achieve better performance than the state-of-the-art baseline for distribution-learning, property optimization, and constrained property optimization tasks but are also computationally efficient. Show Chinese abstract

Abstract: 分子生成需要生成具有所需特性的有效分子，这是一个基本但具有挑战性的任务。近年来，原子级自回归模型迅速发展，这些模型通常通过添加原子级节点和边的顺序操作来构建图。然而，这些原子级模型忽略了高频子结构，这些子结构不仅捕捉分子中原子组合的规律，而且通常与所需的化学性质有关，因此可能在生成高质量分子方面不是最优的。在本文中，我们提出了一种方法，可以从给定的分子图中自动发现此类常见子结构，我们称之为图块。我们还提出了一种基于图块的图块变分自编码器（GP-VAE）用于生成分子图。实验表明，我们的GP-VAE模型不仅在分布学习、属性优化和约束属性优化任务中优于最先进的基线，而且计算效率也较高。