mlsanityData to 7 Oct 2026

Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural Network with Biomedical Network

arXiv 2311.09261 ↗First posted 15 Nov 2023q-bio.QM cs.AI cs.CEcited by 24

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Abstract (arXiv)

Accurately predicting drug-drug interactions (DDI) for emerging drugs, which offer possibilities for treating and alleviating diseases, with computational methods can improve patient care and contribute to efficient drug development. However, many existing computational methods require large amounts of known DDI information, which is scarce for emerging drugs. In this paper, we propose EmerGNN, a graph neural network (GNN) that can effectively predict interactions for emerging drugs by leveraging the rich information in biomedical networks. EmerGNN learns pairwise representations of drugs by extracting the paths between drug pairs, propagating information from one drug to the other, and incorporating the relevant biomedical concepts on the paths. The different edges on the biomedical network are weighted to indicate the relevance for the target DDI prediction. Overall, EmerGNN has higher accuracy than existing approaches in predicting interactions for emerging drugs and can identify the most relevant information on the biomedical network.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 12 of its 44 references that are in the map. First posted on arXiv 15 Nov 2023.

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12 of 44 references.

References · 12

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2018How Powerful are Graph Neural Networks?cited by 4,355
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  4. 2018Link Prediction Based on Graph Neural Networkscited by 829
  5. 2019Composition-based Multi-Relational Graph Convolutional Networkscited by 395
  6. 2017DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoningcited by 265
  7. 2018Modeling polypharmacy side effects with graph convolutional networkscited by 237
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  9. 2020SkipGNN: Predicting Molecular Interactions with Skip-Graph Networkscited by 44
  10. 2020SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarizationcited by 33
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  12. 2021STNN-DDI: A Substructure-aware Tensor Neural Network to Predict Drug-Drug Interactionscited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 10; 2025: 9; 2026: 3.

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  1. 2025GraphMaster: Automated Graph Synthesis via LLM Agents in Data-Limited Environmentscited by 18
  2. 2025Graph Neural Networks in Modern AI-aided Drug Discoverycited by 12
  3. 2023GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropycited by 9
  4. 2024BioMamba: Domain-Adaptive Biomedical Language Modelscited by 8
  5. 2024Learning from Negative Samples in Biomedical Generative Entity Linkingcited by 6
  6. 2025K-Paths: Reasoning over Graph Paths for Drug Repurposing and Drug Interaction Predictioncited by 6
  7. 2024Learning to Describe for Predicting Zero-shot Drug-Drug Interactionscited by 5
  8. 2025Mixture of Length and Pruning Experts for Knowledge Graphs Reasoningcited by 5
  9. 2024Knowledge-Enhanced Recommendation with User-Centric Subgraph Networkcited by 4
  10. 2024Customized Subgraph Selection and Encoding for Drug-drug Interaction Predictioncited by 4
  11. 2025Towards Interpretable Drug-Drug Interaction Prediction: A Graph-Based Approach with Molecular and Network-Level Explanationscited by 3
  12. 2023Improving Biomedical Entity Linking with Retrieval-enhanced Learningcited by 2
12 more
  1. 2024Biomedical Entity Linking as Multiple Choice Question Answeringcited by 2
  2. 2024Benchmarking drug-drug interaction prediction methods: a perspective of distribution changescited by 2
  3. 2024Towards Data-centric Machine Learning on Directed Graphs: a Surveycited by 2
  4. 2025SOLA-GCL: Subgraph-Oriented Learnable Augmentation Method for Graph Contrastive Learningcited by 2
  5. 2024CADGL: Context-Aware Deep Graph Learning for Predicting Drug-Drug Interactionscited by 1
  6. 2025Case-Based Reasoning Enhances the Predictive Power of LLMs in Drug-Drug Interactioncited by 1
  7. 2024Graph Convolutional Network For Semi-supervised Node Classification With Subgraph Sketchingcited by 0
  8. 2025A Survey of Graph Neural Networks for Drug Discovery: Recent Developments and Challengescited by 0
  9. 2025MolBridge: Atom-Level Joint Graph Refinement for Robust Drug-Drug Interaction Event Predictioncited by 0
  10. 2026CrossADR: enhancing adverse drug reactions prediction for combination pharmacotherapy with cross-layer feature integration and cross-level associative learningcited by 0
  11. 2026DDIAgents: Mechanism-Conditioned Context Flow for Drug-Drug Interaction Predictioncited by 0
  12. 2026CoFEND: A Cross-Modal Fusion End-to-End Network for Cold-Start Drug-Drug Interaction Predictioncited by 0