A drug discovery method for anti-COVID-19 based on network characterization

A discovery method and characterization technology, applied in the fields of biological informatics and computer applications, can solve problems such as limited understanding, and achieve the effects of improving performance, improving prediction accuracy, and improving performance

Active Publication Date: 2021-08-17
HUNAN UNIV
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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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  • A drug discovery method for anti-COVID-19 based on network characterization
  • A drug discovery method for anti-COVID-19 based on network characterization
  • A drug discovery method for anti-COVID-19 based on network characterization

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Experimental program
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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 a method for discovering anti-coronavirus inflammatory drugs based on network representation. First, build a multi-source, heterogeneous, large-scale biomedical network by integrating multiple databases such as DrugBank, UniProt, HPRD, SIDER, CTD, NDFRT, and STRING; then, perform sequence sampling in the network to form a network by random walk The sequence library is characterized by using Transformer's deep bidirectional encoder characterization technology to obtain the representation vector of each node; the inductive matrix decomposition technology is used to predict target-drug interactions, discover potential anti-COVID-19 inflammatory drugs, and infer related The mechanism of action of the drug. The present invention provides a multi-layer correlation knowledge for drug research and development by integrating multi-source heterogeneous information and diversified data, thereby improving the prediction accuracy; secondly, the Transformer model integrates the multi-head attention mechanism, which can capture the network in different degrees The correlation between nodes and the physical distance of network nodes can improve the performance of representation.

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 Patents(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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