OpenDDI: A Comprehensive Benchmark for DDI Prediction

• URL: http://arxiv.org/abs/2602.00539v1
• Date: Sat, 31 Jan 2026 06:09:52 GMT
• Title: OpenDDI: A Comprehensive Benchmark for DDI Prediction
• Authors: Xinmo Jin, Bowen Fan, Xunkai Li, Henan Sun, YuXin Zeng, Zekai Chen, Yuxuan Sun, Jia Li, Qiangqiang Dai, Hongchao Qin, Rong-Hua Li, Guoren Wang,
• Abstract summary: Drug-Drug Interactions (DDIs) significantly influence therapeutic efficacy and patient safety.<n>Most studies rely on small-scale DDI datasets and single-modal drug representations.<n>We propose OpenDDI, a comprehensive benchmark for DDI prediction.
• Score: 38.239357319249116
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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

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