通过动态调度专家代理，提升药物相互作用预测的准确性与可解释性。
DDIAgents: Mechanism-Conditioned Context Flow for Drug-Drug Interaction Prediction
- 根据作用机制动态分配相关知识源，实现智能推理路由。
- 在真实数据集上显著优于现有特征、图神经网络及大模型方法。
- 适合需要可解释性AI的药物安全研究与AI4Science领域应用。
药物-药物相互作用（DDI）预测对用药安全至关重要，但需对异构生物医学证据进行推理，而其相关性随作用机制变化。我们提出DDIAgents，一种机制条件化的多智能体框架，通过动态知识编排实现DDI预测。给定药物对后，规划者代理生成专用专家代理，将机制相关的知识源路由至各代理，并由结论代理聚合分析结果。通过根据推断的相互作用机制调整上下文流，DDIAgents减少无关信息干扰，支持互补专家推理，并生成可解释的代理级推理过程。在真实世界DDI预测基准上的大量实验表明，DDIAgents持续优于现有的基于特征、图神经网络、大语言模型及代理基基线方法。除预测性能外，该框架还展示了多智能体系统如何组织异构科学知识，实现适应性与可解释性的AI4Science推理。
原文摘要 · Abstract (English)
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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