计算机工程与应用2026，Vol.62Issue(8)：64-79,16.DOI:10.3778/j.issn.1002-8331.2505-0055
深度学习在药物-药物相互作用预测中的研究进展
Research Developments in Deep Learning for Drug-Drug Interaction Prediction
摘要
Abstract
The risk of drug-drug interaction(DDI)associated with polypharmacy has become increasingly prominent,while traditional experimental approaches,though reliable,are limited by their high cost and lengthy procedures.The pres-ent study focuses on drug feature representation and categorises existing deep learning methods into five types:sequence-based,structured,graph-based,knowledge graph-based,and multimodal fusion approaches.A systematic analysis of their application characteristics,strengths,and limitations is conducted.The experimental findings,derived from the analysis of two benchmark datasets,demonstrate that graph-based and knowledge-graph methods achieve superior performance in complex prediction tasks.Moreover,the merits and constraints of DDI prediction models are analysed across three domains:namely,drug development,clinical application,and decision support.In conclusion,the primary challenges pertaining to data quality,model interpretability and clinical validation are outlined,and the subsequent research direc-tions are discussed.关键词
药物相互作用/深度学习/药物-药物相互作用(DDI)预测/图神经网络(GNN)/卷积神经网络(CNN)/知识图谱(KG)Key words
drug interactions/deep learning/drug-drug interaction(DDI)prediction/graph neural network(GNN)/convo-lutional neural network(CNN)/knowledge graph(KG)分类
信息技术与安全科学引用本文复制引用
申玥玥,史加荣,雍龙泉..深度学习在药物-药物相互作用预测中的研究进展[J].计算机工程与应用,2026,62(8):64-79,16.基金项目
绿色建筑全国重点实验室自主研究课题(LSZZ-Y202414) （LSZZ-Y202414）
陕西省自然科学基础研究计划项目(2024JC-YBMS-014). （2024JC-YBMS-014）
