用强化学习动态融合药物知识，提升罕见药相互作用预测准确率。
Enhanced Drug-drug Interaction Prediction Using Adaptive Knowledge Integration
- 通过强化学习自适应提取并整合药物先验知识
- 少样本学习下显著优于基线模型
- 适合药物安全研究与AI辅助诊疗场景
药物-药物相互作用事件（DDIE）预测对于预防不良反应和确保最佳治疗效果至关重要。然而，现有方法常面临数据不平衡、作用机制复杂以及对未知药物组合泛化能力差等挑战。为此，我们提出一种知识增强框架，通过强化学习技术将先验药物知识自适应地注入大语言模型（LLM），实现知识的动态提取与合成，从而高效优化策略空间，提升LLM在DDIE预测中的准确性。在少样本学习条件下，该方法相比基线取得了显著改进，建立了一个有效的科学知识学习框架用于DDIE预测。
原文摘要 · Abstract (English)
Drug-drug interaction event (DDIE) prediction is crucial for preventing adverse reactions and ensuring optimal therapeutic outcomes. However, existing methods often face challenges with imbalanced datasets, complex interaction mechanisms, and poor generalization to unknown drug combinations. To address these challenges, we propose a knowledge augmentation framework that adaptively infuses prior drug knowledge into a large language model (LLM). This framework utilizes reinforcement learning techniques to facilitate adaptive knowledge extraction and synthesis, thereby efficiently optimizing the strategy space to enhance the accuracy of LLMs for DDIE predictions. As a result of few-shot learning, we achieved a notable improvement compared to the baseline. This approach establishes an effective framework for scientific knowledge learning for DDIE predictions.
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