生物融合DTI:同化图形和序列方式,用于可概括的药物向相互作用预测
Qiufen Chen1, Guanyan Nie2, Xiaoli Li3
1Department of Chemistry, School of Science, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China.
Journal of chemical information and modeling
|December 9, 2025
概括
生物融合DTI是一种新的多式联络深度学习框架,使用集成图形和序列数据准确预测药物向相互作用 (DTI). 它提高了药物发现的概括性和解释性,优于现有的方法.
科学领域:
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
- 人工智能的人工智能
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和重新定位至关重要.
- 当前的深度学习模型在冷启动场景中难以概括,缺乏可解释性.
研究的目的:
- 开发BioFusionDTI,一个多式联网深度学习框架,以提高DTI预测的准确性和可解释性.
- 解决DTI现有的深度学习模型的概括性和可解释性的局限性.
主要方法:
- 整合基于图形 (使用图形卷积网络 - GCN) 和基于序列 (使用卷积神经网络 - CNN) 的药物和蛋白质表示.
- 采用双线注意网络 (BAN) 来捕获细粒度的交叉模式交互.
- 使用预训练的生物分子语言模型进行序列嵌入.
主要成果:
- 在多个基准数据集 (SNAP,DRH,Kinase) 中,BioFusionDTI在各种设置 (热,冷药,冷蛋白) 中始终超过了最先进的基线.
- 废弃性研究证实了融合策略的有效性,BAN模块对性能做出了重大贡献.
- 注意力可视化确定了生物学上可信的相互作用地点,与分子对接结果一致.
结论:
- 生物融合DTI为预测药物向相互作用提供了一个强大而可解释的解决方案.
- 该框架展示了改进的概括能力,特别是在具有挑战性的冷启动场景中.
- 生物融合DTI推进了计算药物发现和重新定位的领域.
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