Recent years have seen a rapid growth of machine learning in cheminformatics problems. In order to tackle the problem of insufficient training data in reality, more and more researchers pay attention to data augmentation technology. However, few researchers pay attention to the problem of construction rules and domain information of data, which will directly impact the quality of augmented data and the augmentation performance. While in graph-based molecular research, the molecular connectivity index, as a critical topological index, can directly or indirectly reflect the topology-based physicochemical properties and biological activities. In this paper, we propose a novel data augmentation technique that modifies the topology of the molecular graph to generate augmented data with the same molecular connectivity index as the original data. The molecular connectivity index combined with data augmentation technology helps to retain more topology-based molecular properties information and generate more reliable data. Furthermore, we adopt five benchmark datasets to test our proposed models, and the results indicate that the augmented data generated based on important molecular topology features can effectively improve the prediction accuracy of molecular properties, which also provides a new perspective on data augmentation in cheminformatics studies.

本文提出了一种新的数据增强技术，通过修改分子图的拓扑结构生成具有与原始数据相同的分子连通性索引的增强数据，结合数据增强技术和分子连通性索引能够保留更多基于拓扑结构的分子性质信息并生成更可靠的数据，通过使用五个基准数据集测试表明，基于重要的分子拓扑特征生成的增强数据可以有效提高分子性质的预测准确度，这也为化学信息学研究中的数据增强提供了新的视角。

多模态表示学习用于分子属性预测：序列、图形、几何