CADGL: Context-Aware Deep Graph Learning for Predicting Drug-Drug Interactions
CoRR(2024)
摘要
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.
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