用知识图增强的多尺度建模用于药物相互作用预测.
Jing Chen1, Qiang Deng2,3, Peimeng Zhen2,3
1School of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, China.
Molecular therapy. Nucleic acids
|February 25, 2026
概括
预测药物相互作用 (DDI) 对患者安全至关重要. 我们的新ALG-DDI模型整合了多尺度的药物信息,大大提高了DDI预测准确性.
科学领域:
- 药理学和生物信息学 药理学和生物信息学
- 人工智能在医学中的应用
背景情况:
- 药物相互作用 (DDI) 的预测对于预防药物不良事件至关重要.
- 当前的机器学习和深度学习模型难以概括和捕捉全面的药物关系.
研究的目的:
- 开发一种先进的模型,ALG-DDI,用于准确预测药物相互作用.
- 整合多样化的药物信息来源,以便对DDI分析采取更全面的方法.
主要方法:
- 提出ALG-DDI,一个多尺度特征融合模型.
- 综合药物属性,局部相关性 (蛋白质,疾病) 和全球语义信息 (PrimeKG).
- 采用了属性掩盖,RGCN,GraphSAGE,ComplEx以及用于特征表示和融合的变压器编码器.
主要成果:
- 与最先进的方法相比,ALG-DDI在三个数据集中表现出更高的性能.
- 广泛的评估证实了该模型的有效性,包括交叉验证和案例研究.
- 该模型成功地扩展到药物相互作用事件预测.
结论:
- ALG-DDI有效地捕获多尺度药物信息,以改善DDI预测.
- 拟议的模型提供了一个强大的解决方案,用于识别潜在的不良药物事件.
- 这种方法推进了计算药理学和个性化医学领域.
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