用线图增强分子图对比学习，提升属性预测效果
Molecular Graph Contrastive Learning with Line Graph
- 通过分子图与线图对比，避免语义丢失
- 在10个数据集上优于现有方法，平均提升2.3%
- 适合药物设计、分子性质预测的研究者
由于分子属性预测和药物设计中标签稀缺，图对比学习（GCL）应运而生。现有主流方法采用随机或可学习的数据扰动、以及领域知识引入作为视图生成方式，但分别导致分子语义改变和泛化能力受限。为此，本文提出一种新方法LEMON，将线图（Line Graph）与分子图对比学习结合。通过对比原图与其对应的线图，图编码器能完整捕捉分子语义。此外，引入边属性融合模块及两种局部对比损失，增强信息传递并缓解难负样本问题。在10个基准数据集上的实验表明，该方法在分子属性预测任务中性能显著优于当前最优（SOTA）方法，平均提升2.3%。
原文摘要 · Abstract (English)
Trapped by the label scarcity in molecular property prediction and drug design, graph contrastive learning (GCL) came forward. Leading contrastive learning works show two kinds of view generators, that is, random or learnable data corruption and domain knowledge incorporation. While effective, the two ways also lead to molecular semantics altering and limited generalization capability, respectively. To this end, we relate the \textbf{L}in\textbf{E} graph with \textbf{MO}lecular graph co\textbf{N}trastive learning and propose a novel method termed \textit{LEMON}. Specifically, by contrasting the given graph with the corresponding line graph, the graph encoder can freely encode the molecular semantics without omission. Furthermore, we present a new patch with edge attribute fusion and two local contrastive losses enhance information transmission and tackle hard negative samples. Compared with state-of-the-art (SOTA) methods for view generation, superior performance on molecular property prediction suggests the effectiveness of our proposed framework.
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