基于结构包括相似性的定向GNN:一种可以控制信息流的方法,以预测药物标结合亲和力
Jipeng Huang1,2,3, Chang Sun1,2,3, Minglei Li1,2,3
1Centre for Bioinformatics and Intelligent Medicine, Nankai University, Tianjin 300071, China.
Bioinformatics (Oxford, England)
|September 18, 2024
概括
我们开发了一种结构包括相似性 (SIS) 方法来预测药物标结合亲和力. 这种方法通过考虑子结构的纳入来提高预测准确性,优于现有的方法.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 生物信息学是一种生物信息学.
背景情况:
- 药物标关联对于药物发现和重新使用至关重要.
- 预测绑定亲和度提供了比二进制分类更详细的见解.
- 现有的方法经常使用对称相似性,忽视了子结构包含关系.
研究的目的:
- 提出一种新的相似性测量方法,以考虑亚结构的包含.
- 开发一个基于图形卷积网络的模型,用于使用这种新的相似性来预测药物标结合亲和力.
主要方法:
- 引入了包括结构的相似性 (SIS),以捕捉分子之间的亚结构包含.
- 建立基于SIS的药物和目标图表.
- 采用图形卷积网络进行结合亲和力预测.
主要成果:
- 基于SIS的方法显著提高了药物标结合亲缘关系预测的准确性.
- 我们的方法与一些最先进的技术相比,表现出更高的性能.
- 案例研究证实了该模型的实际实用性.
结论:
- 考虑将子结构纳入相似度的措施可以增强预测模型.
- 拟议的SIS方法对于预测药物标结合亲缘关系是有效的.
- 这种方法为药物发现和重定向努力提供了有价值的工具.
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