CADGL: Context-Aware Deep Graph Learning for Predicting Drug-Drug Interactions
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866908901024202752 |
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| author | Wasi, Azmine Toushik Rafi, Taki Hasan Islam, Raima Karlo, Serbetar Chae, Dong-Kyu |
| author_facet | Wasi, Azmine Toushik Rafi, Taki Hasan Islam, Raima Karlo, Serbetar Chae, Dong-Kyu |
| contents | 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 |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_17210 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CADGL: Context-Aware Deep Graph Learning for Predicting Drug-Drug Interactions Wasi, Azmine Toushik Rafi, Taki Hasan Islam, Raima Karlo, Serbetar Chae, Dong-Kyu Machine Learning Artificial Intelligence Information Retrieval Biomolecules Molecular Networks 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 |
| title | CADGL: Context-Aware Deep Graph Learning for Predicting Drug-Drug Interactions |
| topic | Machine Learning Artificial Intelligence Information Retrieval Biomolecules Molecular Networks |
| url | https://arxiv.org/abs/2403.17210 |