Graph neural networks (GNNs) have attracted significant attention from the chemical science community because molecules can be represented as a featured graph. In particular, graph convolutional network (GCN) and its variants have been widely used and have shown a state-of-the-art performance in analyzing molecules, such as molecular label classification, drug discovery, and molecular property prediction. However, in molecular analysis, existing GCNs have two fundamental limitations: (1) information of the molecular scale is distorted and (2) global structures in a molecule are ignored. These limitations can seriously degrade the performance in the machine learning-based molecular analysis because the scale and global structure information of a molecule occasionally have a significant effect on the molecular properties. To overcome the limitations of existing GCNs, we comprehensively analyzed the structure of GCNs and developed a costless solution for the limitations of GCNs. To demonstrate the effectiveness of our solution, extensive experiments were conducted on various benchmark datasets.

中文翻译：

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基于图的分子分析的机器学习中无成本的性能改进。

图神经网络（GNN）引起了化学科学界的极大关注，因为分子可以表示为特征图。特别地，图卷积网络（GCN）及其变体已被广泛使用，并且在分析分子方面表现出了最先进的性能，例如分子标记分类，药物发现和分子特性预测。但是，在分子分析中，现有的GCN具有两个基本局限性：（1）分子尺度的信息失真，并且（2）分子中的整体结构被忽略。这些限制会严重降低基于机器学习的分子分析的性能，因为分子的规模和整体结构信息有时会对分子特性产生重大影响。为了克服现有GCN的局限性，我们全面分析了GCN的结构，并为GCN的局限性开发了一种无成本的解决方案。为了证明我们解决方案的有效性，我们对各种基准数据集进行了广泛的实验。