# Benchmarking drug-drug interaction prediction methods: a perspective of distribution changes

arXiv: https://arxiv.org/abs/2410.18583 · HTML: https://mlsanity.com/paper/2410.18583/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 34 references that are in the map.
- First posted on arXiv 24 Oct 2024.

## Builds on (most cited first, 10 of 10)

- [WILDS: A Benchmark of in-the-Wild Distribution Shifts](https://mlsanity.com/paper/2012.07421/) (2020)
- [Analyzing Learned Molecular Representations for Property Prediction](https://mlsanity.com/paper/1904.01561/) (2019)
- [Modeling polypharmacy side effects with graph convolutional networks](https://mlsanity.com/paper/1802.00543/) (2018)
- [Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective](https://mlsanity.com/paper/2003.10780/) (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)
- [Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural Network with Biomedical Network](https://mlsanity.com/paper/2311.09261/) (2023)
- [Lo-Hi: Practical ML Drug Discovery Benchmark](https://mlsanity.com/paper/2310.06399/) (2023)
- [Accurate and interpretable drug-drug interaction prediction enabled by knowledge subgraph learning](https://mlsanity.com/paper/2311.15056/) (2023)
- [Learning to Describe for Predicting Zero-shot Drug-Drug Interactions](https://mlsanity.com/paper/2403.08377/) (2024)

## Built on by (most cited first, 2 of 2)

- [OpenDDI: A Comprehensive Benchmark for DDI Prediction](https://mlsanity.com/paper/2602.00539/) (2026)
- [Resolving Node Identifiability in Graph Neural Processes via Laplacian Spectral Encodings](https://mlsanity.com/paper/2511.19037/) (2025)

## Abstract (arXiv)

Motivation: Emerging drug-drug interaction (DDI) prediction is crucial for new drugs but is hindered by distribution changes between known and new drugs in real-world scenarios. Current evaluation often neglects these changes, relying on unrealistic i.i.d. split due to the absence of drug approval data. Results: We propose DDI-Ben, a benchmarking framework for emerging DDI prediction under distribution changes. DDI-Ben introduces a distribution change simulation framework that leverages distribution changes between drug sets as a surrogate for real-world distribution changes of DDIs, and is compatible with various drug split strategies. Through extensive benchmarking on ten representative methods, we show that most existing approaches suffer substantial performance degradation under distribution changes. Our analysis further indicates that large language model (LLM) based methods and the integration of drug-related textual information offer promising robustness against such degradation. To support future research, we release the benchmark datasets with simulated distribution changes. Overall, DDI-Ben highlights the importance of explicitly addressing distribution changes and provides a foundation for developing more resilient methods for emerging DDI prediction. Availability and implementation: Our code and data are available at https://github.com/LARS-research/DDI-Bench.