在药物相互作用预测中对分子结构,知识图和冷启动场景的系统审查
Mir Mansoor Ahmad1, Zuraini Binti Ali Shah1, Hui Wen Nies1
1Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, 81310, Malaysia.
Computers in biology and medicine
|April 5, 2025
概括
预测药物相互作用 (DDI) 对患者安全至关重要. 本综述探讨了计算方法,包括知识图,以识别潜在的DDI,并解决像冷启动问题这样的挑战,以提高药物安全.
科学领域:
- 药理学和生物信息学 药理学和生物信息学
- 计算机化药物发现技术
背景情况:
- 药物不良相互作用 (DDI) 对健康构成重大风险,影响药物的毒性和治疗效果.
- 临床试验不足以确定所有潜在的DDI,需要计算预测方法.
- 知识图 (KG) 越来越多地用于模拟药物和生物实体之间的复杂关系,用于DDI预测.
研究的目的:
- 审查分子表示和基于知识图的药物相互作用 (DDI) 预测方法.
- 突出在DDI预测中解决冷启动场景的方法.
- 介绍关键发现和改善计算DDI预测的未来方向.
主要方法:
- 在DDI预测中使用的多种分子表示的检查.
- 分析基于知识图 (KG) 的方法,用于整合异构的生物医学数据.
- 讨论在预测模型中减轻冷启动问题的策略.
主要成果:
- 知识图为DDI预测提供了全面的结构和关系见解.
- 由于对某些药物的数据有限,冷启动情景仍然是一个重大挑战.
- 各种计算方法在提高DDI预测准确性和范围方面显示出前景.
结论:
- 计算方法,特别是利用知识图的计算方法,对于预测药物相互作用至关重要.
- 解决冷启动问题对于提高DDI预测模型的通用性和可靠性至关重要.
- 需要对先进的计算技术进行进一步的研究,以提高药物安全性和治疗结果.
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