CAMF-DTI:通过协调注意力和多尺度特征融合来提高药物向相互作用预测
Jia Mi1, Chang Li2, Daguang Jiang1
1College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.
Current issues in molecular biology
|November 26, 2025
概括
通过整合多尺度特征和定向蛋白质信息,CAMF-DTI准确地预测药物向相互作用. 这种新的框架通过改善基准数据集的模型性能来增强药物发现.
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) 预测对于有效的药物发现至关重要.
- 现有的模型在蛋白质序列方向性,位置敏感性和整合多层次结构信息方面扎.
研究的目的:
- 提出CAMF-DTI,一个新的框架,旨在克服当前DTI预测模型的局限性.
- 通过结合协调注意力,多尺度特征融合和交叉注意力来增强药物和蛋白质特征的表示和相互作用学习.
主要方法:
- 药物分子以图形表示,并使用图形卷积网络编码.
- 用协调注意力处理的蛋白质序列,以捕获方向和空间信息.
- 用于集成特征表示和动态DTI建模的多尺度融合和交叉注意模块.
主要成果:
- 在四个基准数据集 (BindingDB,BioSNAP,C.elegans,Human) 中,CAMF-DTI的表现始终超过了七个最先进的基线.
- 在AUROC,AUPRC,精度,F1得分和MCC中观察到性能改善.
- 除研究验证了单个模块的贡献,可视化表明了模型的可解释性.
结论:
- CAMF-DTI有效地模拟定向和多尺度的特征,以改善药物向相互作用的预测.
- 拟议的框架为计算药物发现和开发提供了一个有希望的进步.
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