PDATC-NCPMKL:基于网络一致性投影和多个内核学习,预测药物的解剖治疗化学 (ATC) 代码
Lei Chen1, Jing Xu1, Yubin Zhou2
1College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, China.
Computers in biology and medicine
|December 27, 2023
概括
从现有药物中发现新的药物效应可以加速药物设计. 一个新的计算系统,PDATC-NCPMKL,准确地预测药物-解剖治疗化学 (ATC) 代码关联,改进了现有的模型.
科学领域:
- 药理学和化学信息学
- 计算机化药物发现技术
- 生物信息学是一种生物信息学.
背景情况:
- 药物开发是资源密集的.
- 通过识别新效应来重新利用现有药物是一种具有成本效益的战略.
- 解剖治疗化学品 (ATC) 分类系统有助于理解药物效应.
研究的目的:
- 开发一种先进的计算模型,用于预测药物-ATC代码关联.
- 用现有药物数据提高识别新药效应的准确性.
- 提高药物重用倡议的效率.
主要方法:
- 开发了PDATC-NCPMKL,这是一个整合网络一致性预测和多核学习的推系统.
- 使用多核学习构建和合并多个药物和ATC代码的内核.
- 通过加权的K最近已知的邻居 (WKNKN) 变体,重新构建了药物-ATC关联邻矩阵.
主要成果:
- 获得的接收器运行特征曲线下的面积 (AUROC) 和精度回调曲线下的面积 (AUPR) 值超过0.96.
- 与药物-ATC代码关联预测的现有计算模型相比,表现出卓越的性能.
- 验证了邻近矩阵重制的有效性以及药物和ATC代码内核的意义.
结论:
- PDATC-NCPMKL系统在预测药物-ATC代码关联方面取得了重大进展.
- 这种方法加快了新药效应的识别,促进了药物的重新用途.
- 开发的模型为计算药物发现和药理学研究提供了宝贵的工具.
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