CADGL: Context-Aware Deep Graph Learning for Predicting Drug-Drug Interactions
Not on the map. Why?
Abstract (arXiv)
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
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 24 of its 65 references that are in the map. First posted on arXiv 25 Mar 2024.
Builds on
24 of 65 references.
References · 24
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2018How Powerful are Graph Neural Networks?cited by 4,355
- 2016Variational Graph Auto-Encoderscited by 1,373
- 2021How Attentive are Graph Attention Networks?cited by 818
- 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 751
- 2022GraphMAE: Self-Supervised Masked Graph Autoencoderscited by 443
- 2018GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoderscited by 375
- 2018Modeling polypharmacy side effects with graph convolutional networkscited by 237
- 2021Self-Supervised Learning of Graph Neural Networks: A Unified Reviewcited by 184
- 2018Constrained Graph Variational Autoencoders for Molecule Designcited by 182
- 2021Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learningcited by 130
- 2020GraphNorm: A Principled Approach to Accelerating Graph Neural Network Trainingcited by 116
12 more
- 2022How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervisioncited by 102
- 2017Deformable Shape Completion with Graph Convolutional Autoencoderscited by 50
- 2021Molecular Contrastive Learning with Chemical Element Knowledge Graphcited by 50
- 2020SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarizationcited by 33
- 2023Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural Network with Biomedical Networkcited by 24
- 2019Drug-Drug Adverse Effect Prediction with Graph Co-Attentioncited by 20
- 2022Multi-task Joint Strategies of Self-supervised Representation Learning on Biomedical Networks for Drug Discoverycited by 13
- 2023Comprehensive evaluation of deep and graph learning on drug-drug interactions predictioncited by 10
- 2022Molecular Substructure-Aware Network for Drug-Drug Interaction Predictioncited by 4
- 2022Predicting Drug-Drug Interactions using Deep Generative Models on Graphscited by 4
- 2022Full Kullback-Leibler-Divergence Loss for Hyperparameter-free Label Distribution Learningcited by 3
- 2023Relation-aware graph structure embedding with co-contrastive learning for drug-drug interaction predictioncited by 1
Built on by
Citing papers per year: 2025: 1.
1 paper.