OpenDDI: A Comprehensive Benchmark for DDI Prediction
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

TL;DR
OpenDDI introduces a comprehensive benchmark for drug-drug interaction prediction, unifying datasets, representations, and evaluation protocols to advance research and address current limitations in the field.
Contribution
It unifies multiple datasets, drug representations, and evaluation standards, and introduces new large-scale datasets and multimodal representations for DDI prediction.
Findings
Conducted extensive evaluation revealing key insights.
Identified current limitations in DDI prediction methods.
Provided guidance for future research directions.
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…
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Taxonomy
TopicsComputational Drug Discovery Methods · Machine Learning in Healthcare · Advanced Graph Neural Networks
