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

arXiv: https://arxiv.org/abs/2311.09261 · HTML: https://mlsanity.com/paper/2311.09261/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 44 references that are in the map.
- First posted on arXiv 15 Nov 2023.

## Builds on (most cited first, 12 of 12)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [Composition-based Multi-Relational Graph Convolutional Networks](https://mlsanity.com/paper/1911.03082/) (2019)
- [DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning](https://mlsanity.com/paper/1707.06690/) (2017)
- [Inductive Relation Prediction by Subgraph Reasoning](https://mlsanity.com/paper/1911.06962/) (2019)
- [Modeling polypharmacy side effects with graph convolutional networks](https://mlsanity.com/paper/1802.00543/) (2018)
- [SkipGNN: Predicting Molecular Interactions with Skip-Graph Networks](https://mlsanity.com/paper/2004.14949/) (2020)
- [SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarization](https://mlsanity.com/paper/2010.01450/) (2020)
- [Drug-Drug Interaction Prediction Based on Knowledge Graph Embeddings and Convolutional-LSTM Network](https://mlsanity.com/paper/1908.01288/) (2019)
- [STNN-DDI: A Substructure-aware Tensor Neural Network to Predict Drug-Drug Interactions](https://mlsanity.com/paper/2111.05708/) (2021)

## Built on by (most cited first, 24 of 24)

- [GraphMaster: Automated Graph Synthesis via LLM Agents in Data-Limited Environments](https://mlsanity.com/paper/2504.00711/) (2025)
- [Graph Neural Networks in Modern AI-aided Drug Discovery](https://mlsanity.com/paper/2506.06915/) (2025)
- [GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy](https://mlsanity.com/paper/2312.09708/) (2023)
- [BioMamba: Domain-Adaptive Biomedical Language Models](https://mlsanity.com/paper/2408.02600/) (2024)
- [Learning from Negative Samples in Biomedical Generative Entity Linking](https://mlsanity.com/paper/2408.16493/) (2024)
- [K-Paths: Reasoning over Graph Paths for Drug Repurposing and Drug Interaction Prediction](https://mlsanity.com/paper/2502.13344/) (2025)
- [Learning to Describe for Predicting Zero-shot Drug-Drug Interactions](https://mlsanity.com/paper/2403.08377/) (2024)
- [Mixture of Length and Pruning Experts for Knowledge Graphs Reasoning](https://mlsanity.com/paper/2507.20498/) (2025)
- [Knowledge-Enhanced Recommendation with User-Centric Subgraph Network](https://mlsanity.com/paper/2403.14377/) (2024)
- [Customized Subgraph Selection and Encoding for Drug-drug Interaction Prediction](https://mlsanity.com/paper/2411.01535/) (2024)
- [Towards Interpretable Drug-Drug Interaction Prediction: A Graph-Based Approach with Molecular and Network-Level Explanations](https://mlsanity.com/paper/2507.09173/) (2025)
- [Improving Biomedical Entity Linking with Retrieval-enhanced Learning](https://mlsanity.com/paper/2312.09806/) (2023)
- [Biomedical Entity Linking as Multiple Choice Question Answering](https://mlsanity.com/paper/2402.15189/) (2024)
- [Benchmarking drug-drug interaction prediction methods: a perspective of distribution changes](https://mlsanity.com/paper/2410.18583/) (2024)
- [Towards Data-centric Machine Learning on Directed Graphs: a Survey](https://mlsanity.com/paper/2412.01849/) (2024)
- [SOLA-GCL: Subgraph-Oriented Learnable Augmentation Method for Graph Contrastive Learning](https://mlsanity.com/paper/2503.10100/) (2025)
- [Case-Based Reasoning Enhances the Predictive Power of LLMs in Drug-Drug Interaction](https://mlsanity.com/paper/2505.23034/) (2025)
- [CADGL: Context-Aware Deep Graph Learning for Predicting Drug-Drug Interactions](https://mlsanity.com/paper/2403.17210/) (2024)
- [Graph Convolutional Network For Semi-supervised Node Classification With Subgraph Sketching](https://mlsanity.com/paper/2404.12724/) (2024)
- [A Survey of Graph Neural Networks for Drug Discovery: Recent Developments and Challenges](https://mlsanity.com/paper/2509.07887/) (2025)
- [MolBridge: Atom-Level Joint Graph Refinement for Robust Drug-Drug Interaction Event Prediction](https://mlsanity.com/paper/2510.20448/) (2025)
- [CrossADR: enhancing adverse drug reactions prediction for combination pharmacotherapy with cross-layer feature integration and cross-level associative learning](https://mlsanity.com/paper/2603.15047/) (2026)
- [DDIAgents: Mechanism-Conditioned Context Flow for Drug-Drug Interaction Prediction](https://mlsanity.com/paper/2606.31085/) (2026)
- [CoFEND: A Cross-Modal Fusion End-to-End Network for Cold-Start Drug-Drug Interaction Prediction](https://mlsanity.com/paper/2607.02928/) (2026)

## 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.