# HyGNN: Drug-Drug Interaction Prediction via Hypergraph Neural Network

arXiv: https://arxiv.org/abs/2206.12747 · HTML: https://mlsanity.com/paper/2206.12747/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 47 references that are in the map.
- First posted on arXiv 25 Jun 2022.

## Builds on (most cited first, 14 of 14)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Automatic chemical design using a data-driven continuous representation of molecules](https://mlsanity.com/paper/1610.02415/) (2016)
- [Hypergraph Neural Networks](https://mlsanity.com/paper/1809.09401/) (2018)
- [Modeling polypharmacy side effects with graph convolutional networks](https://mlsanity.com/paper/1802.00543/) (2018)
- [Be More with Less: Hypergraph Attention Networks for Inductive Text Classification](https://mlsanity.com/paper/2011.00387/) (2020)
- [SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarization](https://mlsanity.com/paper/2010.01450/) (2020)
- [CASTER: Predicting Drug Interactions with Chemical Substructure Representation](https://mlsanity.com/paper/1911.06446/) (2019)
- [Bi-Level Graph Neural Networks for Drug-Drug Interaction Prediction](https://mlsanity.com/paper/2006.14002/) (2020)
- [Identifying critical higher-order interactions in complex networks](https://mlsanity.com/paper/2105.02763/) (2021)
- [Hypergraph Laplacians in Diffusion Framework](https://mlsanity.com/paper/2102.08867/) (2021)

## Built on by (most cited first, 16 of 16)

- [A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide](https://mlsanity.com/paper/2404.01039/) (2024)
- [HypeBoy: Generative Self-Supervised Representation Learning on Hypergraphs](https://mlsanity.com/paper/2404.00638/) (2024)
- [A Survey on Hyperlink Prediction](https://mlsanity.com/paper/2207.02911/) (2022)
- [Molecular Hypergraph Neural Networks](https://mlsanity.com/paper/2312.13136/) (2023)
- [LightHGNN: Distilling Hypergraph Neural Networks into MLPs for 100× Faster Inference](https://mlsanity.com/paper/2402.04296/) (2024)
- [HyperGCL: Multi-Modal Graph Contrastive Learning via Learnable Hypergraph Views](https://mlsanity.com/paper/2502.13277/) (2025)
- [DPHGNN: A Dual Perspective Hypergraph Neural Networks](https://mlsanity.com/paper/2405.16616/) (2024)
- [Seq-HyGAN: Sequence Classification via Hypergraph Attention Network](https://mlsanity.com/paper/2303.02393/) (2023)
- [Towards Fair Graph Neural Networks via Graph Counterfactual without Sensitive Attributes](https://mlsanity.com/paper/2412.09947/) (2024)
- [DDI Prediction via Heterogeneous Graph Attention Networks](https://mlsanity.com/paper/2207.05672/) (2022)
- [Tackling Oversmoothing in GNN via Graph Sparsification: A Truss-based Approach](https://mlsanity.com/paper/2407.11928/) (2024)
- [Incorporating Deep Learning Design in Database Queries](https://mlsanity.com/paper/2605.24207/) (2026)
- [HeTriNet: Heterogeneous Graph Triplet Attention Network for Drug-Target-Disease Interaction](https://mlsanity.com/paper/2312.00189/) (2023)
- [HyperSearch: Prediction of New Hyperedges through Unconstrained yet Efficient Search](https://mlsanity.com/paper/2510.17153/) (2025)
- [Hypergraph Pattern Machine: Compositional Tokenization for Higher-Order Interactions](https://mlsanity.com/paper/2605.16527/) (2026)
- [α-Graph: Attention-Infused Normalizing Flow Approach to Tractable Graph Modeling](https://mlsanity.com/paper/2609.07961/) (2026)

## Abstract (arXiv)

Drug-Drug Interactions (DDIs) may hamper the functionalities of drugs, and in the worst scenario, they may lead to adverse drug reactions (ADRs). Predicting all DDIs is a challenging and critical problem. Most existing computational models integrate drug-centric information from different sources and leverage them as features in machine learning classifiers to predict DDIs. However, these models have a high chance of failure, especially for the new drugs when all the information is not available. This paper proposes a novel Hypergraph Neural Network (HyGNN) model based on only the SMILES string of drugs, available for any drug, for the DDI prediction problem. To capture the drug similarities, we create a hypergraph from drugs' chemical substructures extracted from the SMILES strings. Then, we develop HyGNN consisting of a novel attention-based hypergraph edge encoder to get the representation of drugs as hyperedges and a decoder to predict the interactions between drug pairs. Furthermore, we conduct extensive experiments to evaluate our model and compare it with several state-of-the-art methods. Experimental results demonstrate that our proposed HyGNN model effectively predicts DDIs and impressively outperforms the baselines with a maximum ROC-AUC and PR-AUC of 97.9% and 98.1%, respectively.