DDIAgents: Mechanism-Conditioned Context Flow for Drug-Drug Interaction Prediction

Zhenqian Shen; Yu Liu; Xiaoyi Fu; Quanming Yao

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arXiv · 2606.31085

DDIAgents: Mechanism-Conditioned Context Flow for Drug-Drug Interaction Prediction

Zhenqian Shen·Yu Liu·Xiaoyi Fu·Quanming Yao

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Abstract

Drug-drug interaction (DDI) prediction is essential for medication safety, yet it requires reasoning over heterogeneous biomedical evidence whose relevance changes across interaction mechanisms. We propose DDIAgents, a mechanism-conditioned multi-agent framework that performs DDI prediction through dynamic knowledge orchestration. Given a drug pair, a planner agent instantiates specialized expert agents, routes mechanism-relevant knowledge sources to each agent, and aggregates their analyses through a conclusion agent. By adapting context flow to the inferred interaction mechanism, DDIAgents reduces irrelevant information, supports complementary expert reasoning, and produces interpretable agent-level rationales. Extensive experiments on realistic DDI prediction benchmarks show that DDIAgents consistently outperforms existing feature-based, graph-based, LLM-based, and agent-based baselines. Beyond prediction performance, DDIAgents demonstrates how multi-agent systems can organize heterogeneous scientific knowledge for adaptive and interpretable AI4Science reasoning.

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Zhenqian Shen, Yu Liu, Xiaoyi Fu, Quanming Yao. 2026-06-30. DDIAgents: Mechanism-Conditioned Context Flow for Drug-Drug Interaction Prediction. https://arxiv.org/abs/2606.31085

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