Marzouk Nour H, Selim Sahar, Elattar Mustafa, Mabrouk Mai S, Mysara Mohamed
Bioinformatics Group, Centre for Informatics Science (CIS), School of Information Technology and Computer Science (ITCS), Nile University, 12677, Giza, Egypt.
Medical Imaging and Image Processing Group, Centre for Informatics Science (CIS), School of Information Technology and Computer Science (ITCS), Nile University, 12677, Giza, Egypt.
J Cheminform. 2025 Sep 19;17(1):141. doi: 10.1186/s13321-025-01093-2.
In drug development, managing interactions such as drug-drug, drug-disease, and drug-nutrient is critical for ensuring the safety and efficacy of pharmacological treatments. These interactions often overlap, forming a complex, interconnected landscape that necessitates accurate prediction to improve patient outcomes and support evidence-based care. Recent advances in artificial intelligence (AI), powered by large-scale datasets (e.g., DrugBank, TWOSIDES, SIDER), have significantly enhanced interaction prediction. Machine learning, deep learning, and graph-based models show great promise, but challenges persist, including data imbalance, noisy sources, Limited explainability, and underrepresentation of certain types of interactions. This systematic review of 147 studies (2018-2024) is the first to comprehensively map AI applications across major interaction types. We present a detailed taxonomy of models and datasets, emphasizing the growing roles of large language models and knowledge graphs in overcoming key limitations. Their integration-alongside explainable AI tools-enhances transparency, paving the way for AI-driven systems that proactively mitigate adverse interactions. By identifying the most promising approaches and critical research gaps, this review lays the groundwork for advancing more robust, interpretable, and personalized models for drug interaction prediction.
在药物研发中，管理药物与药物、药物与疾病、药物与营养等相互作用对于确保药物治疗的安全性和有效性至关重要。这些相互作用常常相互重叠，形成一个复杂的、相互关联的局面，这就需要进行准确预测，以改善患者预后并支持循证医疗。由大规模数据集（如DrugBank、TWOSIDES、SIDER）驱动的人工智能（AI）的最新进展显著增强了相互作用预测能力。机器学习、深度学习和基于图的模型显示出巨大潜力，但挑战依然存在，包括数据不平衡、噪声源、可解释性有限以及某些类型相互作用的代表性不足。这项对147项研究（2018 - 2024年）的系统综述首次全面梳理了AI在主要相互作用类型中的应用情况。我们提出了一个详细的模型和数据集分类法，强调了大语言模型和知识图谱在克服关键限制方面日益重要的作用。它们与可解释AI工具的整合提高了透明度，为主动减轻不良相互作用的AI驱动系统铺平了道路。通过识别最有前途的方法和关键研究差距，本综述为推进更强大、可解释和个性化的药物相互作用预测模型奠定了基础。
