OpenDDI: A Comprehensive Benchmark for DDI Prediction
Abstract
Drug-Drug Interactions (DDIs) significantly influence therapeutic efficacy and patient safety. As experimental discovery is resource-intensive and time-consuming, efficient computational methodologies have become essential. The predominant paradigm formulates DDI prediction as a drug graph-based link prediction task. However, further progress is hindered by two fundamental challenges: (1) lack of high-quality data: most studies rely on small-scale DDI datasets and single-modal drug representations; (2) lack of standardized evaluation: inconsistent scenarios, varied metrics, and diverse baselines. To address the above issues, we propose OpenDDI, a comprehensive benchmark for DDI prediction. Specifically, (1) from the data perspective, OpenDDI unifies 6 widely used DDI datasets and 2 existing forms of drug representation, while additionally contributing 3 new large-scale LLM-augmented datasets and a new multimodal drug representation covering 5 modalities. (2) From the evaluation perspective, OpenDDI unifies 20 SOTA model baselines across 3 downstream tasks, with standardized protocols for data quality, effectiveness, generalization, robustness, and efficiency. Based on OpenDDI, we conduct a comprehensive evaluation and derive 10 valuable insights for DDI prediction while exposing current limitations to provide critical guidance for this rapidly evolving field. Our code is available at https://github.com/xiaoriwuguang/OpenDDI
Cite
@article{arxiv.2602.00539,
title = {OpenDDI: A Comprehensive Benchmark for DDI Prediction},
author = {Xinmo Jin and Bowen Fan and Xunkai Li and Henan Sun and YuXin Zeng and Zekai Chen and Yuxuan Sun and Jia Li and Qiangqiang Dai and Hongchao Qin and Rong-Hua Li and Guoren Wang},
journal= {arXiv preprint arXiv:2602.00539},
year = {2026}
}