PS3N:利用蛋白质序列结构相似性来发现新的药物相互作用
Saminur Islam1, Ahmed Abbasi2, Nitin Agarwal3
1Department of Computer Science, North Carolina State University, Raleigh, NC, USA. sislam8@ncsu.edu.
Scientific reports
|October 24, 2025
概括
这项研究引入了一个新的神经网络框架,PS3N,使用蛋白质序列和结构来预测药物相互作用 (DDI). 该模型表现出高精度,改善了药物安全监督.
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:
- 药物不良事件,特别是药物相互作用 (DDI),对公共卫生构成重大挑战.
- 目前用于DDI预测的计算方法通常依赖于各种药物信息和机器学习算法.
- 药物安全概况和监测需要准确预测潜在的DDI.
研究的目的:
- 通过利用遗传信息,开发一种新的计算框架来预测药物相互作用 (DDI).
- 利用药物点的蛋白质序列和结构来提高DDI预测.
- 确定具有潜在临床意义的新型DDI.
主要方法:
- 提出了一个基于相似性的神经网络框架,称为蛋白序列结构相似性网络 (PS3N).
- 该框架整合了药物属性,蛋白质标,蛋白质序列和蛋白质结构.
- 药物对药物的相似性是使用不同数据类别的多个相似度指标计算的.
主要成果:
- PS3N 模型在各种数据集中实现了高预测性能.
- 关键性能指标包括精度 (91%-98%),回忆力 (90%-96%),F1评分 (86%-95%),AUC (88%-99%) 和准确性 (86%-95%).
- 该模型成功预测了新的DDI,其中一些具有确定的临床意义.
结论:
- 通过使用蛋白序列和结构信息,PS3N框架有效地预测药物相互作用.
- 这种方法为改善药物安全监督和分析提供了一个有前途的工具.
- 鉴定新的DDI突出了PS3N模型的临床实用性.
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