实现可解释的相互作用预测:将生物层次结构嵌入到超标相互作用空间中
Domonkos Pogány1, Péter Antal1
1Department of Measurement and Information Systems, Budapest University of Technology and Economics, Budapest, Hungary.
PloS one
|March 21, 2024
概括
这项研究引入了可解释的药物向相互作用预测模型,通过将生物层次结构集成到超标潜伏空间中. 这种方法提高了模型的可解释性,并有助于药物发现,而不会牺牲预测准确性.
科学领域:
- 计算生物学 计算生物学
- 药理学 药理学是指药理学的学科.
- 机器学习 机器学习
背景情况:
- 传统药物开发是缓慢而昂贵的.
- 计算方法加速药物发现,但往往缺乏可解释性.
- 用于药物向相互作用预测的机器学习模型需要改进可解释性.
研究的目的:
- 开发更易于解释的药物向相互作用预测模型.
- 将生物层次结构 (药物和蛋白质) 整合到一个联合嵌入的潜空间中.
- 为了进行交互预测,比较欧几里德和过度的嵌入空间.
主要方法:
- 开发了一个基于相似性的预测模型,在与生物层次结构一致的潜空间中.
- 综合药物和蛋白质层次使用嵌入规范化.
- 进行了欧几里德式与超标式嵌入的比较分析.
- 用于潜在空间分析和可视化而使用的维度缩小.
主要成果:
- 层次规范化可以提高模型的解释性,而不会降低预测性能.
- 超标嵌入,与规范化,提高嵌入生物层次的质量.
- 拟议的方法提供了对模型潜伏空间的视觉洞察.
结论:
- 可解释的超模隐藏空间有助于明智的药物发现应用.
- 整合层次结构和使用夸张几何学可以提高药物向相互作用预测的透明度.
- 该方法与现有的可解释AI解决方案兼容,以进一步提高透明度.
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