一个基于学习的生物特征和异质网络表示框架,用于药物向相互作用预测
Liwei Liu1,2, Qi Zhang1, Yuxiao Wei3
1College of Science, Dalian Jiaotong University, Dalian 116028, China.
Molecules (Basel, Switzerland)
|September 28, 2023
概括
BG-DTI使用生物特征和网络分析预测药物向相互作用 (DTI). 这种基于学习的框架改进了现有的方法,帮助药物发现和重新定位努力.
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.
- 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) 的预测对于有效的药物发现至关重要.
- 传统的DTI测定实验方法是昂贵的,耗时的,劳动密集的.
- 开发计算方法对于加速药物发现管道至关重要.
研究的目的:
- 引入BG-DTI,一种基于学习的新框架,用于预测药物向相互作用.
- 整合生物序列特征与异质网络信息,以提高DTI预测.
- 为实验性DTI确定提供更有效,更准确的替代方案.
主要方法:
- 使用序列衍生生物特征编码药物和目标.
- 构建药物-药物和目标-目标相似性网络以捕捉生物关系.
- 使用图形卷积网络 (GCN) 和图形注意网络 (GAT) 进行特征表示学习.
- 在合并的描述符上使用随机森林分类器进行最终DTI预测.
主要成果:
- BG-DTI实现了0.938的高平均AUC和0.930.93的AUPR.
- 拟议的框架优于现有的五种最先进的DTI预测方法.
- 证明了结合序列特征和网络信息用于DTI预测的有效性.
结论:
- BG-DTI提供了一种强大而准确的计算工具,用于预测药物向相互作用.
- 该框架有潜力显著促进药物发现和药物改用倡议.
- 将图形表示学习与生物特征的整合代表了DTI预测研究的一个有希望的方向.
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