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增强信息交叉注意力融合用于药物标结合亲缘关系预测
Ailu Fei1, Yihan Wang2, Tiantian Ruan2
1School of Information Science and Technology, Nantong University, Nantong, China.
PeerJ. Computer science
|September 24, 2025
概括
这项研究介绍了CAFIE-DTA,这是一种用于药物标亲和力预测的新型深度学习模型. 通过整合3D蛋白质结构和化合物特征,CAFIE-DTA提高了预测准确度,优于现有方法.
科学领域:
- 人工智能的人工智能
- 计算化学的计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 深度学习显著提高了药物发现效率,特别是在预测药物向亲和力 (DTA) 方面.
- 传统的DTA预测方法往往忽视了蛋白质和化合物的关键的3D结构和物理化学特性.
研究的目的:
- 开发一种新的深度学习模型,CAFIE-DTA,用于改进药物向亲和力预测.
- 将3D蛋白质结构信息 (曲率,静电电位) 和化合物物理化学特征纳入DTA预测中.
主要方法:
- 拟议的CAFIE-DTA模型使用Delaunay三角法来近似蛋白质表面曲率,并使用APBS软件来计算静电电位.
- 采用跨多头关注融合蛋白序列,物理化学信息和3D结构性质.
- 使用类似的注意力机制整合化合物的图形和物理化学特征.
主要成果:
- 与现有方法相比,CAFIE-DTA在戴维斯和KIBA基准数据集上表现优异.
- 在两个数据集上,在R平方和平均平方误差 (MSE) 中取得了显著的改进.
- 超越传统模型的性能,仅依赖2D结构和序列数据进行DTA预测.
结论:
- CAFIE-DTA提供了一种更准确,更全面的方法来预测药物向 afinity.
- 整合3D结构和物理化学信息对于推进DTA预测模型至关重要.
- 开发的模型显示了加速药物发现和开发管道的前景.
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