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Network pharmacology study on anti-stroke of Xiaoshuan Tongluo formula based on systematic compound-target interaction prediction models
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Yi-fu ZHENG1, 2, Ling-lei KONG1, Hao JIA1, Bao-yue ZHANG1, Zhe WANG1, Lü-jie XU1, Ai-liu LIU1, *, Guan-hua DU1, *
Acta Pharmaceutica Sinica | 2020, 55(2) : 256 - 264
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Acta Pharmaceutica Sinica | 2020, 55(2): 256-264
Original Articles
Network pharmacology study on anti-stroke of Xiaoshuan Tongluo formula based on systematic compound-target interaction prediction models
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Yi-fu ZHENG1, 2, Ling-lei KONG1, Hao JIA1, Bao-yue ZHANG1, Zhe WANG1, Lü-jie XU1, Ai-liu LIU1, *, Guan-hua DU1, *
Affiliations
  • 1. Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
  • 2. College of Pharmacy, Wuhan University, Wuhan 430072, China
Published: 2020-02-12 doi: 10.16438/j.0513-4870.2019-0521
Outline
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Xiaoshuan Tongluo formula is effective in treating mental retardation and speech astringency caused by cerebral thrombosis, but its mechanism remains unclear. In this investigation, by collecting the chemical constituents from Xiaoshuan Tongluo formula and the targets related to stroke, we obtained 1 251 constituents from the formula and 10 drug targets related with stroke. We established 18 prediction models of compound-target interaction for 10 stroke-related targets, using molecular docking method and machine learning methods includes Naive Bayesian and recursive partitioning based on the input of molecular fingerprints and molecular descriptors. Using these models, we predicted the active chemical constituents from Xiaoshuan Tongluo formula and their drug targets, 153 potential active constituents were discovered, 22 among them could interact with at least two drug targets related with stroke. On this basis, the chemical constituent-target network was constructed using network construction software, and then the important metabolic pathways of the targets were identified by using Gene-Ontology (GO) enrichment analysis, such as blood coagulation, positive regulation of angiogenesis, positive regulation of ion transport and so on. On this basis, a target-pathway network was constructed. In conclusion, using machine learning, molecular docking, virtual screening, data mining and network construction, this study explored and partially revealed the active chemical constituents and chemical constituent-target-pathway network action mechanism of Xiaoshuan Tongluo formula against stroke, which will provide important information for its further study.

Xiaoshuan Tongluo formula  /  stroke  /  drug target  /  machine learning  /  molecular docking  /  virtual screening  /  network pharmacology
Yi-fu ZHENG, Ling-lei KONG, Hao JIA, Bao-yue ZHANG, Zhe WANG, Lü-jie XU, Ai-liu LIU, Guan-hua DU. Network pharmacology study on anti-stroke of Xiaoshuan Tongluo formula based on systematic compound-target interaction prediction models[J]. Acta Pharmaceutica Sinica, 2020 , 55 (2) : 256 -264 . DOI: 10.16438/j.0513-4870.2019-0521
Year 2020 volume 55 Issue 2
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Article Info
doi: 10.16438/j.0513-4870.2019-0521
  • Receive Date:2019-06-30
  • Online Date:2026-01-20
  • Published:2020-02-12
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History
  • Received:2019-06-30
  • Revised:2019-09-23
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Affiliations
    1. Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
    2. College of Pharmacy, Wuhan University, Wuhan 430072, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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