在广泛的药物相互作用预测中,人工智能应用的全面景观:系统性审查
Nour H Marzouk1, Sahar Selim2, Mustafa Elattar2
1Bioinformatics Group, Centre for Informatics Science (CIS), School of Information Technology and Computer Science (ITCS), Nile University, 12677, Giza, Egypt.
Journal of cheminformatics
|September 20, 2025
概括
人工智能 (AI) 正在彻底改变药物相互作用的预测,提高安全性和有效性. 本综述绘制了人工智能应用的地图,强调了大型语言模型和知识图,以实现更强大,更易于解释的药物安全系统.
科学领域:
- 药理学和计算科学 药理学和计算科学
- 药物发现和开发 药物发现和开发
- 医疗保健中的人工智能
背景情况:
- 管理药物-药物,药物-疾病和药物-营养物相互作用对于安全有效的药物开发至关重要.
- 这些相互作用形成了一个复杂的网络,需要准确的预测,以改善患者的治疗结果和基于证据的护理.
- 人工智能 (AI) 和大规模数据集已经推进了交互预测,但数据不平衡和有限的解释性等挑战仍然存在.
研究的目的:
- 在主要的药物相互作用类型中系统地审查和映射AI应用.
- 提供用于交互预测的AI模型和数据集的全面分类.
- 确定有前途的AI方法和研究差距,以推进药物相互作用预测.
主要方法:
- 2018年至2024年间发表的147项研究的系统文献综述.
- 分析人工智能模型,包括机器学习,深度学习和基于图形的方法.
- 数据集的分类和新兴趋势的识别,如大型语言模型和知识图.
主要成果:
- 人工智能显著提高了药物相互作用预测,机器学习,深度学习和基于图形的模型显示出有希望.
- 大型语言模型和知识图越来越多地被用于克服数据不平衡和缺乏解释性等局限性.
- 正在整合可解释的AI工具,以提高AI驱动的药物相互作用预测系统的透明度.
结论:
- 人工智能,特别是大型语言模型和知识图,为预测复杂的药物相互作用提供了强大的解决方案.
- 解决数据不平衡和可解释性等挑战是开发强大和可解释的AI模型的关键.
- 本综述为人工智能驱动的个性化药物安全和相互作用管理的未来研究提供了基础.
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