The traditional Chinese medicines (TCMs) have been used to treat diseases over a long history, but it is still a great challenge to uncover the underlying mechanisms for their therapeutic effects due to the complexity of their ingredients. Based on a novel network pharmacology-based approach, we explored in this study the potential therapeutic targets of Liuwei Dihuang (LWDH) decoction in its neuroendocrine immunomodulation (NIM) function. We not only collected the known targets of the compounds in LWDH but also predicted the targets for these compounds using the balanced substructure-drug-target network-based inference (bSDTNBI), which is a target prediction method based on network inferring developed by our laboratory. A “target-(pathway)-target” (TPT) network, in which targets of LWDH were connected by relevant pathways, was constructed and divided into several separate modules with strong internal connections. Then the target module that contributes the most to NIM function was determined through a contribution scoring algorithm. Finally, the targets with the highest contribution score to NIM-related diseases in this target module were recommended as potential therapeutic targets of LWDH.

中文翻译：

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从网络药理学看六味地黄汤的分子机制

中药治疗疾病的历史由来已久，但由于其成分的复杂性，揭示其治疗作用的潜在机制仍是一项巨大的挑战。基于一种新的基于网络药理学的方法，我们在这项研究中探索了六味地黄（LWDH）汤在其神经内分泌免疫调节（NIM）功能中的潜在治疗靶点。我们不仅收集了LWDH中化合物的已知靶标，还使用平衡子结构-药物-靶标网络推理（bSDTNBI）预测了这些化合物的靶标，这是我们实验室开发的一种基于网络推理的靶标预测方法. “目标-（途径）-目标”（TPT）网络，其中 LWDH 的目标通过相关途径连接，被构建并分成几个独立的模块，具有强大的内部连接。然后通过贡献评分算法确定对NIM功能贡献最大的目标模块。最后，该目标模块中对 NIM 相关疾病贡献得分最高的目标被推荐为 LWDH 的潜在治疗目标。