Compared with the general complex network, the multilayer network is more suitable for the description of reality. It can be used as a tool of network pharmacology to analyze the mechanism of drug action from an overall perspective. Combined with random walk algorithm, it measures the importance of nodes from the entire network rather than a single layer. Here a four-layer network was constructed based on the data about the action process of prescriptions, consisting of ingredients, target proteins, metabolic pathways and diseases. The random walk algorithm was used to calculate the betweenness centrality of the protein layer nodes to get the rank of their importance. According to above method, we screened out the top 10% proteins that play a key role in treatment. Prescriptions Xiaochaihu Decoction was taken as example to prove our method. The selected proteins were measured with the ones that have been validated to be associated with the treated diseases. The results showed that its accuracy was no less than the topology-based method of single-layer network. The applicability of our method was proved by another prescription Yupingfeng Decoction. Our study demonstrated that multilayer network combined with random walk algorithm was an effective method for pre-screening vital target proteins related to prescriptions.

与一般的复杂网络相比，多层网络更适合描述现实。它可以用作网络药理学的工具，从整体角度分析药物作用的机制。结合随机游走算法，它可以测量整个网络而非单个层中节点的重要性。在此，根据有关处方作用过程的数据构建了一个四层网络，包括成分，靶蛋白，代谢途径和疾病。随机游走算法用于计算蛋白质层节点之间的居中性，从而确定其重要性等级。根据上述方法，我们筛选出了在治疗中起关键作用的前10％的蛋白质。方药小柴胡汤为例，证明了本方法的有效性。用已被证实与所治疗的疾病有关的蛋白质来测量所选蛋白质。结果表明，其准确性不低于基于拓扑的单层网络方法。另一种处方玉屏风汤证明了本方法的适用性。我们的研究表明，多层网络结合随机游走算法是一种有效筛选与处方相关的重要靶蛋白的有效方法。