基于异质性图和数据增强的蛋白质 - 配体相互作用预测
Weimin Li1, Xiaoyang Li1, Mengying Wang1
1School of Computer Engineering and Science, Shanghai University, Shanghai, China.
Journal of biomolecular structure & dynamics
|March 12, 2025
概括
这项研究引入了一种新的深度学习模型,HGEF-Net,用于预测蛋白质-连接体相互作用,提高药物发现效率. 异质图增强融合网络 (HGEF-Net) 通过利用图形结构和数据增强来提高具有挑战性的数据集的性能.
科学领域:
- 计算化学是一种计算化学.
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
背景情况:
- 预测蛋白质-配体相互作用对于药物发现至关重要.
- 现有的深度学习模型在稀疏,不平衡的数据上扎,并未充分利用元数据.
- 计算方法在捕捉结构动态方面往往是密集和有限的.
研究的目的:
- 开发一种先进的深度学习模型,用于准确预测蛋白质-连接体相互作用.
- 在稀疏和不平衡的生物数据集上增强模型性能.
- 提高药物发现和重新定位管道的效率和有效性.
主要方法:
- 提出了异质图增强融合网络 (HGEF-Net) 模型.
- 使用异质信息学习模块进行子图分析和元数据集成.
- 实施了数据增强的多层次对比学习策略.
- 为特征融合开发了一个异质的关注框架.
主要成果:
- 与BindingDB和Davis数据集上的最先进模型相比,HGEF-Net表现出更高的性能.
- 在BindingDB.上获得了0.826的AUC和0.811的AUPRC.
- 数据增强模块显著改善了戴维斯数据集中的关键指标 (AUC,AUPRC,精度,回忆).
结论:
- 通过整合异质图形信息和先进的数据增强技术,HGEF-Net有效地捕捉了复杂的生物相互作用.
- 该模型显示了加速药物发现的巨大潜力,通过提供准确和高效的蛋白质-连接体相互作用预测.
- 提出的方法解决了现有模型在处理稀疏和不平衡的生物数据方面的局限性.
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