CADGL: Context-Aware Deep Graph Learning for Predicting Drug-Drug Interactions

• URL: http://arxiv.org/abs/2403.17210v2
• Date: Wed, 27 Mar 2024 21:47:49 GMT
• Title: CADGL: Context-Aware Deep Graph Learning for Predicting Drug-Drug Interactions
• Authors: Azmine Toushik Wasi, Taki Hasan Rafi, Raima Islam, Serbetar Karlo, Dong-Kyu Chae,
• Abstract summary: Examining Drug-Drug Interactions (DDIs) is a pivotal element in the process of drug development. We aim to address 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. We excel in predicting clinically valuable novel DDIs, supported by rigorous case studies.
• Score: 5.648318448953635
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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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