4.7 Article

Targets preliminary screening for the fresh natural drug molecule based on Cosine-correlation and similarity-comparison of local network

期刊

JOURNAL OF TRANSLATIONAL MEDICINE
卷 20, 期 1, 页码 -

出版社

BMC
DOI: 10.1186/s12967-022-03279-w

关键词

Targets screening; Fresh natural drug molecule; Cosine-correlation; Similarity-comparison; Western-Blot

资金

  1. National Nature Science Foundation of China [82074269, 61872297]
  2. Shaanxi Provincial Key Research & Development Program, China [2020KW-063]
  3. Fundamental Research Funds for the Central public welfare research institutes [Z0708]

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This study proposes a novel method based on Cosine-correlation and Similarity-comparison of Local Network (CSLN) for preliminary screening and assignment of weights to targets of fresh natural drug molecules. The results show that CSLN outperforms existing prediction models and can be an alternative strategy for screening targets of fresh natural drug molecules.
Background Chinese herbal medicine is made up of hundreds of natural drug molecules and has played a major role in traditional Chinese medicine (TCM) for several thousand years. Therefore, it is of great significance to study the target of natural drug molecules for exploring the mechanism of treating diseases with TCM. However, it is very difficult to determine the targets of a fresh natural drug molecule due to the complexity of the interaction between drug molecules and targets. Compared with traditional biological experiments, the computational method has the advantages of less time and low cost for targets screening, but it remains many great challenges, especially for the molecules without social ties. Methods This study proposed a novel method based on the Cosine-correlation and Similarity-comparison of Local Network (CSLN) to perform the preliminary screening of targets for the fresh natural drug molecules and assign weights to them through a trained parameter. Results The performance of CSLN is superior to the popular drug-target-interaction (DTI) prediction model GRGMF on the gold standard data in the condition that is drug molecules are the objects for training and testing. Moreover, CSLN showed excellent ability in checking the targets screening performance for a fresh-natural-drug-molecule (scenario simulation) on the TCMSP (13 positive samples in top20), meanwhile, Western-Blot also further verified the accuracy of CSLN. Conclusions In summary, the results suggest that CSLN can be used as an alternative strategy for screening targets of fresh natural drug molecules.

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