Drug-drug interactions (DDIs) are a leading cause of preventable adverse events, often complicating treatment and increasing healthcare costs. At the same time, knowing which drugs do not interact is equally important, as such knowledge supports safer prescriptions and better patient outcomes. In this study, we propose an interpretable and efficient framework that blends modern machine learning with domain knowledge to improve DDI prediction. Our approach combines two complementary molecular embeddings - Mol2Vec, which captures fragment-level structural patterns, and SMILES-BERT, which learns contextual chemical features - together with a leakage-free, rule-based clinical score (RBScore) that injects pharmacological knowledge without relying on interaction labels. A lightweight neural classifier is then optimized using a novel three-stage metaheuristic strategy (RSmpl-ACO-PSO), which balances global exploration and local refinement for stable performance. Experiments on real-world datasets demonstrate that the model achieves high predictive accuracy (ROC-AUC 0.911, PR-AUC 0.867 on DrugBank) and generalizes well to a clinically relevant Type 2 Diabetes Mellitus cohort. Beyond raw performance, studies show how embedding fusion, RBScore, and the optimizer each contribute to precision and robustness. Together, these results highlight a practical pathway for building reliable, interpretable, and computationally efficient models that can support safer drug therapies and clinical decision-making.

翻译：药物相互作用是导致可预防不良事件的主要原因，常使治疗复杂化并增加医疗成本。同时，了解哪些药物不发生相互作用同样重要，因为此类知识有助于实现更安全的处方和更好的患者预后。本研究提出了一种可解释且高效的框架，将现代机器学习与领域知识相结合以改进DDI预测。我们的方法融合了两种互补的分子嵌入——捕获片段级结构模式的Mol2Vec与学习上下文化学特征的SMILES-BERT，并结合了基于无泄漏规则设计的临床评分（RBScore），该评分在不依赖相互作用标签的情况下注入了药理学知识。随后采用新型三阶段元启发式策略（RSmpl-ACO-PSO）优化轻量级神经分类器，该策略通过平衡全局探索与局部优化来确保稳定性能。在真实数据集上的实验表明，该模型实现了较高的预测准确度（在DrugBank上ROC-AUC达0.911，PR-AUC达0.867），并能良好泛化至临床相关的2型糖尿病队列。除原始性能外，研究还揭示了嵌入融合、RBScore及优化器各自对精度与鲁棒性的贡献。这些成果共同为构建可靠、可解释且计算高效的模型指明了实用路径，此类模型有望为更安全的药物治疗与临床决策提供支持。