系统:基于"传统中医药-成分-标"的相互作用的药理作用的客观识别的系统网络平台
Zewen Wang1, Mengqi Huo1, Liansheng Qiao1
1Key Laboratory of TCM-information Engineer of State Administration of TCM, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China.
Computers in biology and medicine
|July 23, 2024
概括
这项研究介绍了SYSTCM,这是一种使用机器学习的新平台,可以客观地识别传统中医药 (TCM) 的效果和疗效,推动TCM药物开发.
科学领域:
- 计算化学是一种计算化学.
- 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:
- 分析传统中医 (TCM) 机制的传统方法依赖于专家经验,缺乏客观框架.
- 深度学习和机器学习为识别TCM药理学影响提供了客观的方法.
研究的目的:
- 开发一个客观的计算框架来识别TCM药理学效果和疗效.
- 建立一个系统网络平台 (SYSTCM),将这些预测整合到TCM药物开发中.
主要方法:
- 构建了分子描述符和药物标之间的相互作用网络图.
- 使用googLeNet卷积神经网络开发了药理效应 (IPE) 的最佳识别模型.
- 使用支持矢量机器 (SVM) 创建了18个用于TCM疗效 (RTE) 的识别模型.
主要成果:
- 在IPE模型中,AUC>0.8,MCC>0.7,ACC>0.85.8均为AUC.
- 该SYSTCM平台整合了70,961个术语,包括TCM,成分,药理学效果和疗效.
- SYSTCM成功预测了具有抗炎作用的成分,确定了Coptidis Rhizoma成分的药理作用,并阐明了Salviae Miltiorrhizae radix et rhizome的成分,作用和疗效.
结论:
- SYSTCM提供了一个客观的方法来确定TCM的药理学影响,减少主观性.
- 该平台促进了TCM药物开发,并通过阐明复杂的关系来增强临床应用.
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