DBN algorithm based drug targeting protein action prediction method

A prediction method and drug technology, applied in neural learning methods, calculations, special data processing applications, etc., to achieve the effect of saving financial and material resources and saving time

Inactive Publication Date: 2018-12-07
SOUTH CHINA AGRI UNIV
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art, and propose a method for predicting the effect of drug-targeted proteins based on the DBN algorithm, whic...

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  • DBN algorithm based drug targeting protein action prediction method
  • DBN algorithm based drug targeting protein action prediction method
  • DBN algorithm based drug targeting protein action prediction method

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Embodiment Construction

[0040] The present invention will be further described below in conjunction with specific examples.

[0041] like figure 1 As shown, the DBN algorithm-based drug targeting protein effect prediction method provided in this embodiment includes the following steps:

[0042]Step S1, calculating the extended connection connectivity fingerprint feature of the drug and the tripeptide structural feature of the amino acid sequence of the protein, and concatenating the two. In DTI prediction, it is necessary to judge whether the drug and the protein will interact. This is actually a binary classification problem. The drug and protein are combined to form a drug-protein pair, which is divided into two categories: interaction and no interaction. interaction. DBN is usually used to solve classification problems. It takes the feature vector of the object as input, and finally outputs the classification label. In DTI prediction, the feature vectors of drugs and proteins are calculated sepa...

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Abstract

The invention discloses a DBN algorithm based drug targeting protein action prediction method. The method comprises the steps of extracting extended communication fingerprint of the drug by starting with the molecular structure of the drug; extracting tripeptide structural characteristic of the protein by starting with amino acid sequence of the protein, splicing the extended communication fingerprint of the drug and the tripeptide structural characteristic of the protein to compose a drug-protein characteristic vector, then inputting the drug-protein characteristic vector to a depth confidence network, wherein the output of the network is the probability of the drug-protein input by the network for the mutual action, and finally selecting a suitable threshold to judge the pair of the association is set up. The DBN algorithm based drug targeting protein action prediction method can give out possible interaction pair of drug-targeted protein rapidly without manual intervention, can accordingly save drug development and testing costs and speeds up drugging and discovery of new functions of drugs.

Description

technical field [0001] The present invention relates to the technical fields of artificial intelligence, machine learning, deep learning and precision medicine, in particular to a method for predicting the effect of a drug-targeted protein based on a DBN algorithm. Background technique [0002] Protein is an important substance in biological organisms, and it is the main bearer of life activities. The vast majority of drugs have an effect on the human body by interacting with proteins after entering the body. Drugs and biomacromolecules combine and interact with each other and act on the drug target. Drug action targets include: enzymes, ion channels, receptors, etc., which are all composed of proteins. Therefore, to study the mechanism of drug-protein interaction, Conducive to people's life and health safety. [0003] Traditional drug-targeted protein research uses biological experiments to confirm whether there is an interaction between the drug and the protein, which re...

Claims

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Application Information

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IPC IPC(8): G06F19/00G06F19/18G06F19/24G06N3/04G06N3/08
CPCG06N3/08G06N3/084G06N3/045
Inventor 古万荣毛宜军田绪红黎嘉朗
Owner SOUTH CHINA AGRI UNIV
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