In the field of molecular biology, graph representation learning is crucial for molecular structure analysis. However, challenges arise in recognising functional groups and distinguishing isomers due to a lack of spatial structure information. To address these problems, we design a novel graph representation learning method based on a spatial structure information extraction Transformer (SSET). The SSET model comprises the Edge Feature Fusion Subgraph Spatial Structure Extractor (ETSE) module and the Positional Information Encoding Graph Transformer (PEGT) module. The ETSE module extracts spatial structural information by fusing edge features and generating the most-value subgraph (Mv-subgraph). The PEGT module encodes positional information based on the graph transformer, addressing the indistinguishability problem among nodes with identical features. In addition, the SSET model alleviates the burden of high computational complexity by using subgraph. Experiments on real datasets show that the SSET model, built on the graph transformer, considerably improves graph representation learning.

在分子生物学领域，图表示学习对于分子结构分析至关重要。然而，由于缺乏空间结构信息，在识别官能团和区分异构体方面出现了挑战。为了解决这些问题，我们设计了一种基于空间结构信息提取变压器（SSET）的新颖的图表示学习方法。 SSET模型包括边缘特征融合子图空间结构提取器（ETSE）模块和位置信息编码图变换器（PEGT）模块。 ETSE模块通过融合边缘特征并生成最值子图（Mv-subgraph）来提取空间结构信息。 PEGT模块基于图变换器对位置信息进行编码，解决具有相同特征的节点之间的不可区分问题。此外，SSET模型通过使用子图减轻了高计算复杂度的负担。对真实数据集的实验表明，基于图转换器构建的 SSET 模型显着改善了图表示学习。