SaeGraphDTI:基于序列属性提取和图形神经网络的药物向相互作用预测
Qiaosheng Zhang1,2, Zhenyu Sun3, Zhaoman Zhong3
1School of Computer Engineering, Jiangsu Ocean University, No. 59, Cangwu Road, Haizhou District, Lianyungang, 222000, Jiangsu, China. zqs@jou.edu.cn.
BMC bioinformatics
|July 16, 2025
概括
SaeGraphDTI通过将序列特征与图形神经网络集成来提高药物向相互作用 (DTI) 的预测. 这种方法提高了准确性,加速了药物开发,降低了成本.
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.
- 药物发现 药物发现
背景情况:
- 准确的药物向相互作用 (DTI) 识别对于有效的药物开发至关重要.
- 目前用于DTI预测的深度学习模型严重依赖于有效的特征提取.
- 药物和目标网络包含有价值的拓信息,用于增强特征表示.
研究的目的:
- 开发一种新的深度学习模型,用于预测药物向相互作用 (DTI).
- 为了利用基于序列的特性和网络拓来改进DTI预测.
- 引入SaeGraphDTI,一个结合序列属性提取和图形神经网络的模型.
主要方法:
- 使用序列特征提取器来获得药物和目标序列的特性.
- 现有的关系网络使用相似关系来增强.
- 一个图形编码器更新节点信息,接着是用于DTI概率计算的图形解码器.
主要成果:
- 拟议的SaeGraphDTI模型与最先进的方法相比显示出更高的性能.
- 该模型在四个公共数据集上的多个关键指标中取得了最佳结果.
- SaeGraphDTI有效地利用序列属性和网络拓来进行准确的预测.
结论:
- SaeGraphDTI在预测潜在的药物向相互作用方面表现出强大的能力.
- 该模型是加速药物开发管道的宝贵工具.
- 这些发现突出了整合序列和网络信息用于DTI预测的潜力.
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