Wang Zihao, Lin Hui-Heng, Linghu Kegang, Huang Run-Yue, Li Guangyao, Zuo Huali, Yu Hua, Chan Ging, Hu Yuanjia
State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau.
The Second Clinical College, Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine.
Chem Pharm Bull (Tokyo). 2019;67(8):778-785. doi: 10.1248/cpb.c18-00808.
Herbal formulae have a long history in clinical medicine in Asia. While the complexity of the formulae leads to the complex compound-target interactions and the resultant multi-target therapeutic effects, it is difficult to elucidate the molecular/therapeutic mechanism of action for the many formulae. For example, the Hua-Yu-Qiang-Shen-Tong-Bi-Fang (TBF), an herbal formula of Chinese medicine, has been used for treating rheumatoid arthritis. However, the target information of a great number of compounds from the TBF formula is missing. In this study, we predicted the targets of the compounds from the TBF formula via network analysis and in silico computing. Initially, the information of the phytochemicals contained in the plants of the herbal formula was collected, and subsequently computed to their corresponding fingerprints for the sake of structural similarity calculation. Then a compound structural similarity network infused with available target information was constructed. Five local similarity indices were used and compared for their performance on predicting the potential new targets of the compounds. Finally, the Preferential Attachment Index was selected for it having an area under curve (AUC) of 0.886, which outperforms the other four algorithms in predicting the compound-target interactions. This method could provide a promising direction for identifying the compound-target interactions of herbal formulae in silico.
草药配方在亚洲临床医学中有着悠久的历史。虽然配方的复杂性导致了复杂的化合物-靶点相互作用以及由此产生的多靶点治疗效果，但很难阐明许多配方的分子/治疗作用机制。例如，中药草药配方化瘀强身通痹方（TBF）已被用于治疗类风湿性关节炎。然而，TBF配方中大量化合物的靶点信息缺失。在本研究中，我们通过网络分析和计算机模拟计算预测了TBF配方中化合物的靶点。首先，收集草药配方中植物所含植物化学物质的信息，随后计算其相应的指纹图谱以进行结构相似性计算。然后构建一个注入可用靶点信息的化合物结构相似性网络。使用并比较了五个局部相似性指标在预测化合物潜在新靶点方面的性能。最后，选择优先附着指数是因为其曲线下面积（AUC）为0.886，在预测化合物-靶点相互作用方面优于其他四种算法。该方法可为在计算机上识别草药配方的化合物-靶点相互作用提供一个有前景的方向。
