Examining Drug-Drug Interactions (DDIs) is a pivotal element in the process
of drug development. DDIs occur when one drug's properties are affected by the
inclusion of other drugs. Detecting favorable DDIs has the potential to pave
the way for creating and advancing innovative medications applicable in
practical settings. However, existing DDI prediction models continue to face
challenges related to generalization in extreme cases, robust feature
extraction, and real-life application possibilities. We aim to address these
challenges by leveraging the effectiveness of context-aware deep graph learning
by introducing a novel framework named CADGL. Based on a customized variational
graph autoencoder (VGAE), we capture critical structural and physio-chemical
information using two context preprocessors for feature extraction from two
different perspectives: local neighborhood and molecular context, in a
heterogeneous graphical structure. Our customized VGAE consists of a graph
encoder, a latent information encoder, and an MLP decoder. CADGL surpasses
other state-of-the-art DDI prediction models, excelling in predicting
clinically valuable novel DDIs, supported by rigorous case studies.

使用 CADGL 框架通过上下文感知的深度图学习来解决现有药物相互作用（DDI）预测模型在极端情况下的泛化问题、鲁棒特征提取问题和实际应用可能性问题。基于定制化的变分图自编码器（VGAE），我们从局部邻域和分子上下文两个不同角度的上下文预处理器中提取临界的结构和物理化学信息，并在异质图结构中进行特征提取。CADGL 超越了其他最先进的 DDI 预测模型，在预测临床上有价值的新型 DDIs 方面表现出色，并得到严格的案例研究支持。