SGcCA:通过一个端到端模型解读药物-标相互作用,使用空间和通道重建卷积和交叉效率-添加注意力
Lihong Peng1, Wen Liao1, Zejun Li2
1School of Computer Science and Artificial Intelligence, Hunan University of Technology, Zhuzhou 412007, Hunan, China.
Journal of chemical information and modeling
|October 9, 2025
概括
一个新的框架SGcCA通过使用深度学习准确预测药物向相互作用 (DTI) 来增强药物重新定位. 它在DTI预测方面表现优于现有的模型,为研究人员提供了有价值的工具.
科学领域:
- 计算生物学是一种计算生物学.
- 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:
- 药物向相互作用 (DTI) 的预测对于药物重新定位至关重要,但传统方法昂贵且耗时.
- 深度学习为DTI预测提供了先进的功能,但在特征提取和融合方面仍然存在挑战.
- 现有的DTI预测模型在准确学习和整合药物和蛋白质表示方面存在局限性.
研究的目的:
- 引入SGcCA,用于增强DTI预测的端到端框架.
- 提高用于药物重新定位应用的DTI预测的准确性和效率.
- 为了解决药物和蛋白质特征学习和融合在DTI预测中的局限性.
主要方法:
- 开发了SGcCA,集成空间和通道重建卷积 (SCConv),图形卷积网络 (GCN) 和交叉效率增量注意力 (CEAA).
- 通过减少冗余,利用SCConv编码药物 (SMILES) 和蛋白质 (氨基酸序列) 特性.
- 使用GCN从二维分子图中提取药物特征,并使用CEAA进行有效的特征融合.
主要成果:
- 在四个数据集 (Human,C.elegans,BindingDB,DrugBank) 的六个已建立的DTI预测模型中,SGcCA表现出卓越的性能.
- 该框架实现了更高的准确性,F1分数,MCC,AUROC和AUPRC,表明了更好的解释性和概括性.
- 除研究证实了SCConv,CEAA和GCN成分的显著贡献;分子对接验证了预测的相互作用.
结论:
- SGcCA为DTI预测提供了强大而有效的解决方案,大大推进了药物重新定位的努力.
- 该框架的卓越性能和可解释性使其成为识别新型药物向相互作用的宝贵工具.
- SGcCA可作为一个开源工具来支持药物发现和重新定位社区.
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