  AI-driven drug design relies significantly on predicting molecular
properties, which is a complex task. In current approaches, the most commonly
used feature representations for training deep neural network models are based
on SMILES and molecular graphs. While these methods are concise and efficient,
they have limitations in capturing complex spatial information. Recently,
researchers have recognized the importance of incorporating three-dimensional
information of molecular structures into models. However, capturing spatial
information requires the introduction of additional units in the generator,
bringing additional design and computational costs. Therefore, it is necessary
to develop a method for predicting molecular properties that effectively
combines spatial structural information while maintaining the simplicity and
efficiency of graph neural networks. In this work, we propose an embedding
approach CTAGE, utilizing $k$-hop discrete Ricci curvature to extract
structural insights from molecular graph data. This effectively integrates
spatial structural information while preserving the training complexity of the
network. Experimental results indicate that introducing node curvature
significantly improves the performance of current graph neural network
frameworks, validating that the information from k-hop node curvature
effectively reflects the relationship between molecular structure and function.
