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跨模态交互意识的渐进融合网络用于药物向相互作用预测
Zhichong Cao1, Jing Xie2, Junlin Xu1
1School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, China.
Journal of chemical information and modeling
|September 19, 2025
概括
这项研究引入了一种新的深度学习框架,即跨模式交互感知渐进融合网络 (CIPFN),以提高药物向相互作用 (DTI) 的预测. CIPFN有效地捕获和融合双向DTI信息,提高识别潜在候选药物的准确性.
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.
- 机器学习是机器学习.
背景情况:
- 药物向相互作用 (DTI) 的预测对于加速药物发现至关重要.
- 深度学习模型已经推进了DTI预测,但在捕获和融合双向交叉模式信息方面仍然存在挑战.
- 现有的方法难以有效地整合用于DTI分析的各种数据模式.
研究的目的:
- 提出一种新的深度学习融合框架,即跨模式交互意识的渐进融合网络 (CIPFN),以改进DTI预测.
- 解决捕获和融合双向DTI信息的现有方法的局限性.
- 提高识别潜在药物标关系的准确性和效率.
主要方法:
- 开发了一个名为CIPFN的深度学习融合框架.
- 引入了一种双向相互作用意识模块,用于细粒度药物蛋白相互作用对齐.
- 实施了带有门和卷积块的渐进融合网络,用于提取关键信息.
主要成果:
- 在五个基准数据集中,CIPFN表现出显著的改进.
- 与最先进的方法相比,在AUROC,AUPRC,F1中实现了卓越的性能,灵敏度和精度.
- 有效地捕获和融合跨模式信息,以提高DTI预测.
结论:
- 拟议的CIPFN框架为DTI预测提供了一个强大的方法.
- 在DTI分析中,CIPFN有效地解决了双向信息融合的挑战.
- 这种方法有望通过改进的DTI识别来加速药物发现管道.
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