基于关系相似性的图形对比学习用于DTI预测.
Jilong Bian1, Hao Lu1, Limin Wei1
1College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, Heilongjiang, China.
Briefings in bioinformatics
|March 24, 2025
概括
这项研究引入了一种新方法RSGCL-DTI,通过结合结构和关系特征来改善药物向相互作用 (DTI) 的预测. 这种方法提高了药物重定向的准确性,并优于现有的模型.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 药物发现 药物发现 药物发现
背景情况:
- 准确的药物向相互作用 (DTI) 预测对于有效的药物重定向至关重要.
- 现有的用于DTI预测的深度学习方法通常只关注结构或关系特征,限制性能.
- 整合不同的特征类型可以显著提高DTI预测的准确性.
研究的目的:
- 开发一个先进的DTI预测模型,利用药物和蛋白质的结构和关系特征.
- 通过增强的DTI预测,提高药物重定向的准确性和效率.
- 在DTI预测中引入一种新的图形对比学习方法,用于特征提取.
主要方法:
- 为DTI预测提出的基于关系相似性的图形对比学习 (RSGCL-DTI).
- 在异构的药物蛋白相互作用网络上使用图形对比学习提取了蛋白质间和药物间的关系特征.
- 将提取的关系特征与D-MPNN和CNN的结构特征相结合,以获得全面的特征表示.
主要成果:
- 与四个基准数据集的八个最先进的基线模型相比,RSGCL-DTI模型表现出更高的性能.
- 拟议的方法在不平衡的数据集上表现强,这是DTI预测的一个常见挑战.
- RSGCL-DTI表现出极好的概括能力,有效地预测了未见的药物-蛋白质对的相互作用.
结论:
- 将图形对比学习衍生的关系特征与结构特征相结合,可以显著提高DTI预测.
- RSGCL-DTI提供了一种更准确,更可靠的药物重定向方法.
- 该模型的强大性能和概括能力突出显示了其在现实世界药物发现应用中的潜力.
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