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. CADGL is vailable at: https://github.com/azminewasi/cadgl

翻译：检测药物间相互作用是药物开发过程中的关键环节。当一种药物的特性因其他药物的加入而受到影响时，即发生药物相互作用（DDI）。识别有益的DDI有望为开发并推进适用于实际场景的创新药物铺平道路。然而，现有DDI预测模型在极端情况下的泛化能力、稳健特征提取以及现实应用可行性方面仍面临挑战。我们旨在通过引入一种名为CADGL的新框架，利用上下文感知深度图学习的有效性来应对这些挑战。基于定制化变分图自编码器（VGAE），我们采用两种上下文预处理器从不同视角——局部邻域和分子上下文——在异构图结构中提取关键的结构与理化信息。定制化VGAE由图编码器、隐信息编码器和MLP解码器组成。CADGL超越了其他最先进的DDI预测模型，在预测具有临床价值的新型DDI方面表现卓越，相关结论得到了严格案例研究的支持。CADGL的代码可在https://github.com/azminewasi/cadgl获取。