Anti-COVID-19 drug discovery method based on network characterization

A discovery method and characterization technology, applied in the field of computer applications and bioinformatics, can solve problems such as limited understanding, and achieve the effect of improving performance, improving prediction accuracy, and improving performance

Active Publication Date: 2021-02-02
HUNAN UNIV
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  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, due to the current limited understanding of COVID-19 target information and pathology, drug repositioning methods for the treatment of COVID-19 face numerous challenges and problems

Method used

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  • Anti-COVID-19 drug discovery method based on network characterization
  • Anti-COVID-19 drug discovery method based on network characterization
  • Anti-COVID-19 drug discovery method based on network characterization

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Embodiment Construction

[0039] The present invention will be further described below in conjunction with the accompanying drawings.

[0040] refer to figure 1 , a method for discovering anti-coronavirus inflammatory drugs based on network characterization, the method comprising the following steps:

[0041] 1) Parameter initialization, including setting the number of sequence trajectories psize, network sequence length l, node reading threshold deg, representation vector dimension dim, Transformer encoder layer number n;

[0042] 2) Build a drug heterogeneous information network;

[0043] 3) Randomly select psize∈[1,num] and psize∈N + nodes as the initial sampling node x of each sequence trajectory j ∈{x i |i=1,2,...,num}, sequentially sample the network node sequence according to the specific semantic path;

[0044] 4) Perform word segmentation on all sampled sequences, including unicode string conversion, remove special characters, space word segmentation, remove redundant characters and punct...

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Abstract

The invention belongs to the field of computer science, and discloses an anti-COVID-19 drug discovery method based on network characterization. The method comprises the following steps: firstly, constructing a multi-source, heterogeneous and large-scale biological medicine network by fusing a plurality of databases such as DugBank, UniProt, HPRD, SIDER, CTD, NDFRT and STRING; then, performing sequence sampling in the network in a random walk mode to form a network sequence library, and performing characterization by utilizing a deep bidirectional encoder characterization technology of Transformer to obtain a characterization vector of each node is obtained; and performing target drug interaction prediction by using an induction matrix decomposition technology so as to find a potential anti-COVID19 drug and to infer action mechanism of related drugs. According to the method, multi-source heterogeneous information and diversified data are integrated to provide multi-layer associated knowledge for drug research and development, so that the prediction precision is improved; secondly, a multi-head attention mechanism is fused through a Transformer model, the relevance between network nodes and the physical distance between the network nodes can be captured to different degrees, and then the characterization performance is improved.

Description

technical field [0001] The invention relates to the fields of biological informatics and computer applications, and in particular to a method for discovering anti-coronavirus inflammatory drugs based on network characterization. Background technique [0002] The outbreak and rapid spread of COVID-19 pose a serious threat to global health, and studies have shown that the host's excessive immune response is an important factor leading to acute respiratory distress syndrome (ARDS) in patients with COVID-19. Although many researchers are committed to understanding the pathogenic mechanism of SARS-CoV-2 and developing related drugs to control and prevent SARS-CoV-2; however, many studies have focused on predicting the life cycle of SARS-CoV-2 Proteins or drugs to reveal the pathogenesis and treatment options of viral infections. However, recent studies have shown that the development of severe disease does not seem to be only related to viral load, and the excessive inflammatory...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16B50/30G06F40/30G16B30/00G16B25/00G16H70/40
CPCG16H70/40G16B25/00G16B30/00G16B50/30G06F40/30
Inventor 彭绍亮王小奇李非辛彬杨亚宁向伟铭李介臣
Owner HUNAN UNIV
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