内源性大麻素系统包括多种植物大麻素、大麻素受体和酶，它们协助发挥任何药理作用所需的许多步骤。众所周知，内源性大麻素系统抑制炎症和自身免疫疾病类风湿性关节炎（RA）的发病机制。据我们所知，尚未有研究解释基于网络药理学的以内源性大麻素系统为重点的抗风湿过程。因此，本研究的目的是进一步了解以丰富的自然内源性大麻素为基础的RA信号通路、相关蛋白质和基因。有关Cannabis sativa中植物大麻素如何影响内源性大麻素系统的知识是从文献中收集的。使用SwissTarget预测和BindingDB数据库来预测植物大麻素的靶点。与RA相关的基因从DisGeNET和GeneCards数据库中检索。蛋白质-蛋白质相互作用（高置信度>0.7）通过string网络服务器完成，并使用Cytoscape显示。采用京都基因和基因组百科全书（KEGG）代谢通路分析对内源性大麻素-RA共同靶点进行富集分析。使用ShinyGO 0.76预测基因本体（GO）分类系统中列出的生物学过程。采用分子对接、诱导适应性对接和分子动力学模拟精确地了解配体与受体之间的结合亲和力。网络药理学分析预测，响应含氧化合物和肽胺酸修饰等过程与RA的潜在治疗机制有关。这些生物作用由癌症、神经活性配体-受体相互作用、脂质和动脉硬化、钙信号通路以及Rap1信号通路协同调节。根据分子对接的结果，在RA的情况下，植物大麻素可能与重要的靶蛋白如PIK3CA、AKT1、MAPK9、PRKCD、BRAF、IGF1R和NOS3结合。整个研究预测了植物大麻素的系统生物学特性。但是，未来需要进行实验研究以确认到目前为止的结果。

The endocannabinoid system consists of several phytocannabinoids, cannabinoid receptors, and enzymes that aid in numerous steps necessary to manifest any pharmacological activity. It is well known that the endocannabinoid system inhibits the pathogenesis of the inflammatory and autoimmune disease rheumatoid arthritis (RA). To the best of our knowledge, no research has been done that explains the network-pharmacology-based anti-rheumatic processes by focusing on the endocannabinoid system. Therefore, the purpose of this study is to further our understanding of the signaling pathways, associated proteins, and genes underlying RA based on the abundant natural endocannabinoids. The knowledge on how the phytocannabinoids in Cannabis sativa affect the endocannabinoid system was gathered from the literature. SwissTarget prediction and BindingDB databases were used to anticipate the targets for the phytocannabinoids. The genes related to RA were retrieved from the DisGeNET and GeneCards databases. Protein-protein interactions (high confidence > 0.7) were carried out with the aid of the string web server and displayed using Cytoscape. The Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathway analysis was used to perform enrichment analyses on the endocannabinoid-RA common targets. ShinyGO 0.76 was used to predict the biological processes listed in the Gene Ontology (GO) classification system. The binding affinity between the ligand and the receptors was precisely understood using molecular docking, induced-fit docking, and a molecular dynamics simulation. The network pharmacology analyses predicted that processes like response to oxygen-containing compounds and peptodyl-amino acid modification are related to the potential mechanisms of treatment for RA. These biological actions are coordinated by cancer, neuroactive ligand-receptor interaction, lipids and atherosclerosis, the calcium signaling pathway, and the Rap1 signaling pathway. According to the results of molecular docking, in the context of RA, phytocannabinoids may bind to important target proteins such PIK3CA, AKT1, MAPK9, PRKCD, BRAF, IGF1R, and NOS3. This entire study predicted the phytocannabinoids' systemic biological characteristics. Future experimental research is needed, however, to confirm the results so far.