Article ; Online: Novel Compound-Target Interactions Prediction for the Herbal Formula Hua-Yu-Qiang-Shen-Tong-Bi-Fang.
Chemical & pharmaceutical bulletin
2019 Volume 67, Issue 8, Page(s) 778–785
Abstract: ... For example, the Hua-Yu-Qiang-Shen-Tong-Bi-Fang (TBF), an herbal formula of Chinese medicine, has been used ...
Abstract | 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. |
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MeSH term(s) | Algorithms ; Arthritis, Rheumatoid/drug therapy ; Drug Compounding ; Drug Interactions ; Drugs, Chinese Herbal/chemistry ; Drugs, Chinese Herbal/therapeutic use ; Humans ; Medicine, Chinese Traditional |
Chemical Substances | Drugs, Chinese Herbal |
Language | English |
Publishing date | 2019-07-31 |
Publishing country | Japan |
Document type | Journal Article |
ZDB-ID | 213307-6 |
ISSN | 1347-5223 ; 0009-2363 |
ISSN (online) | 1347-5223 |
ISSN | 0009-2363 |
DOI | 10.1248/cpb.c18-00808 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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