Abstract：
Mechanism analysis is essential for the use and promotion of Traditional Chinese Medicine (TCM). Traditional methods of network analysis relying on expert experience lack an explanatory framework, prompting the application of deep learning and machine learning for objective identification of TCM pharmacological effects. A dataset was used to construct an interacted network graph between 424 molecular descriptors and 465 pharmacological targets to represent the relationship between components and pharmacological effects. Subsequently, the optimal identification model of pharmacological effects (IPE) was established through convolution neural networks of GoogLeNet structure. The AUC values are greater than 0.8, MCC values are greater than 0.7, and ACC values are greater than 0.85 across various test datasets. Subsequently, 18 recognition models of TCM efficacy (RTE) were created using support vector machines (SVM). Integration of pharmacological effects and efficacies led to the development of the systemic web platform for identification of pharmacological effects (SYSTCM). The platform, comprising 70,961 terms, including 636 Traditional Chinese Medicines (TCMs), 8190 components, 40 pharmacological effects, and 18 efficacies. Through the SYSTCM platform, (1) Total 100 components were predicted from TCMs with anti-inflammatory pharmacological effects. (2) The pharmacological effects of complete constituents were predicted from Coptidis Rhizoma (Huang Lian). (3) The principal components, pharmacological effects, and efficacies were elucidated from Salviae Miltiorrhizae radix et rhizome (Dan Shen). SYSTCM addresses subjectivity in pharmacological effect determination, offering a potential avenue for advancing TCM drug development and clinical applications. Access SYSTCM at http://systcm.cn.
摘要：
机理分析对于中药的使用和推广至关重要。传统的依靠专家经验的网络分析方法缺乏解释框架，提示应用深度学习和机器学习对中药药理作用进行客观识别。使用数据集来构建424个分子描述符和465个药理学靶标之间的交互网络图,以表示组分和药理学作用之间的关系。随后，利用GoogLeNet结构的卷积神经网络建立药理作用最佳鉴定模型(IPE)。在各种测试数据集上,AUC值大于0.8,MCC值大于0.7,并且ACC值大于0.85。随后，使用支持向量机（SVM）创建了18个中医疗效（RTE）识别模型。药理作用和功效的整合导致了用于识别药理作用（SYSTCM）的系统网络平台的开发。平台,包括70,961个术语，包括636种中药，8190组件，40药理作用，18种功效通过SYSTCM平台，（1）从具有抗炎药理作用的TCM中预测了总共100种成分。(2)黄连完整成分的药理作用预测。(3)主成分，药理作用，并阐明了丹参（丹参）的功效。SYSTCM解决了药理作用测定中的主观性，为推进中药药物开发和临床应用提供了潜在的途径。在http://systcm访问SYSTCM。cn.
