# DDIPrompt: Drug-Drug Interaction Event Prediction based on Graph Prompt Learning

arXiv: https://arxiv.org/abs/2402.11472 · HTML: https://mlsanity.com/paper/2402.11472/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 47 references that are in the map.
- First posted on arXiv 18 Feb 2024.

## Builds on (most cited first, 6 of 6)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Universal Prompt Tuning for Graph Neural Networks](https://mlsanity.com/paper/2209.15240/) (2022)
- [PRODIGY: Enabling In-context Learning Over Graphs](https://mlsanity.com/paper/2305.12600/) (2023)
- [HGPROMPT: Bridging Homogeneous and Heterogeneous Graphs for Few-shot Prompt Learning](https://mlsanity.com/paper/2312.01878/) (2023)

## Built on by (most cited first, 7 of 7)

- [Does Graph Prompt Work? A Data Operation Perspective with Theoretical Analysis](https://mlsanity.com/paper/2410.01635/) (2024)
- [RELIEF: Reinforcement Learning Empowered Graph Feature Prompt Tuning](https://mlsanity.com/paper/2408.03195/) (2024)
- [Graph Prompting for Graph Learning Models: Recent Advances and Future Directions](https://mlsanity.com/paper/2506.08326/) (2025)
- [Towards Graph Prompt Learning: A Survey and Beyond](https://mlsanity.com/paper/2408.14520/) (2024)
- [Pre-trained Prompt-driven Semi-supervised Local Community Detection](https://mlsanity.com/paper/2505.12304/) (2025)
- [GP2F: Cross-Domain Graph Prompting with Adaptive Fusion of Pre-trained Graph Neural Networks](https://mlsanity.com/paper/2602.11629/) (2026)
- [Enhanced Drug-drug Interaction Prediction Using Adaptive Knowledge Integration](https://mlsanity.com/paper/2603.12885/) (2026)

## Abstract (arXiv)

Drug combinations can cause adverse drug-drug interactions(DDIs). Identifying specific effects is crucial for developing safer therapies. Previous works on DDI event prediction have typically been limited to using labels of specific events as supervision, which renders them insufficient to address two significant challenges: (1) the bias caused by highly imbalanced event distribution where certain interaction types are vastly under-represented. (2) the scarcity of labeled data for rare events, a pervasive issue where rare yet potentially critical interactions are often overlooked or under-explored due to limited available data. In response, we offer ``DDIPrompt'', an innovative solution inspired by the recent advancements in graph prompt learning. Our framework aims to address these issues by leveraging the intrinsic knowledge from pre-trained models, which can be efficiently deployed with minimal downstream data. Specifically, to solve the first challenge, DDIPrompt features a hierarchical pre-training strategy to foster a generalized and comprehensive understanding of drug properties. It captures intra-molecular structures through augmented links based on structural proximity between drugs, further learns inter-molecular interactions emphasizing edge connections rather than concrete catagories. For the second challenge, we implement a prototype-enhanced prompting mechanism during inference. This mechanism, refined by few-shot examples from each category, effectively harnesses the rich pre-training knowledge to enhance prediction accuracy, particularly for these rare but crucial interactions. Extensive experiments on two benchmark datasets demonstrate DDIPrompt's SOTA performance, especially for those rare DDI events.