基于深度学习和知识图的药物相互作用预测:一篇综述
Huimin Luo1,2, Weijie Yin1, Jianlin Wang1,3
1School of Computer and Information Engineering, Henan University, Kaifeng, China.
iScience
|February 26, 2024
概括
使用深度学习和知识图的计算方法可以有效预测药物相互作用 (DDI),为传统分析提供更快的替代方案. 本综述探讨了用于DDI预测的这些先进技术.
科学领域:
- 药理学 药理学是指药理学的学科.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 药物相互作用 (DDI) 具有重大风险,导致不可预测的效应和不良事件.
- 传统的DDI检测方法通常是昂贵且耗时的.
- 需要有效的计算方法来预测DDI.
研究的目的:
- 系统地审查使用深度学习和知识图表技术对药物相互作用预测的研究.
- 总结可用的生物医学数据和与DDI预测相关的公共药物数据库.
- 分析和比较现有的计算DDI预测方法.
主要方法:
- 基于深度学习的DDI预测方法的审查.
- 基于知识图的DDI预测方法的审查.
- 综合深度学习和知识图的混合方法的分析.
- 在基准数据集上的预测方法的比较.
主要成果:
- 将DDI预测方法分为三个主要类别:基于深度学习,基于知识图表和混合方法.
- 对药物数据和预测方法的全面分析.
- 对各种DDI预测技术的比较性能评估.
结论:
- 深度学习和知识图表技术在推进DDI预测方面表现有前途.
- 需要进一步的研究来解决诸如不对称和高阶DDI预测等挑战.
- 计算DDI预测为传统方法提供了更有效的替代方案.
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