LGABAN:一种集成的多尺度方法,结合图形和序列特征,用于增强药物蛋白相互作用的预测
Yi Wen1, Shiyu Yan1, Min Chen2
1School of Computer, University of South China, HengYang 421001, China.
Journal of chemical information and modeling
|December 18, 2025
概括
这项研究介绍了LGABAN,这是一种用于预测药物向相互作用的新型深度学习框架. 通过模拟药物和蛋白质之间的复杂,多尺度的关系,LGABAN提高了准确性和可解释性.
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) 识别对于有效的药物研究至关重要.
- 目前用于DTI预测的深度学习方法与复杂交互的特征表示和可解释性作斗争.
研究的目的:
- 提出一种新的深度学习框架,LGABAN,用于改进药物向相互作用预测.
- 在DTI预测中解决特征表示和模型可解释性方面的挑战.
主要方法:
- LGABAN采用双分支结构,从药物和蛋白质中提取本地和全球特征.
- 双线性注意网络 (BAN) 整合了四种类型的特征对 (本地-本地,本地-全球,全球-本地,全球-全球) 来建模多层次交互.
- 一个多头图注意力网络 (GAT) 增强了药物图表的表示.
主要成果:
- 与六个最先进的基线模型相比,LGABAN在四个公共数据集中表现出卓越的性能.
- 该框架在药物向相互作用预测的各个方面取得了令人满意的可解释性.
结论:
- 对于药物向相互作用的预测,LGABAN提供了一种强大且可解释的深度学习方法.
- 拟议的框架可以大大帮助加快药物发现和降低研究成本.
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