Drug protein binding rate prediction method and system based on structure and grade classification

A technology of protein binding rate and grade classification, which is applied in the field of prediction of drug protein binding rate of grade classification, can solve the problem of low prediction accuracy of high binding drugs, achieve the effect of reducing research and development risks, improving prediction accuracy, and increasing applicability

Active Publication Date: 2020-01-14
FUDAN UNIV
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Problems solved by technology

[0007] The technical problem to be solved by the present invention is t

Method used

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  • Drug protein binding rate prediction method and system based on structure and grade classification
  • Drug protein binding rate prediction method and system based on structure and grade classification
  • Drug protein binding rate prediction method and system based on structure and grade classification

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Embodiment

[0040] This embodiment provides a method for predicting drug protein binding rate based on structure and rank classification, such as figure 1 shown, including:

[0041] Step 1, data collection and processing:

[0042] (1) By consulting literature and databases, a total of 12,646 protein binding rate (PPB) data values ​​and corresponding structural codes of drug molecules were collected.

[0043] (2) Process the collected data:

[0044] If the collected PPB value is within a numerical range (for example: 0.96~0.97), take the mean value of the numerical range as the PPB value of the drug molecule; if the collected PPB value is greater than or less than a certain value (for example: >0.99) , if there is a more reliable data source, select the PPB value from a more reliable source as the PPB value of the drug molecule, if not, take the fixed value 0.99 as the PPB value of the drug molecule.

[0045] According to the naming, structure coding and properties of the drug molecule,...

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Abstract

The invention relates to a drug protein binding rate prediction method and system based on structure and grade classification. The method comprises the steps of: (1) collecting data, processing a collected PPB data value, and removing repeated drug molecules; (2) dividing the PPB values of the drug molecules into three levels of data sets of high-binding drugs, medium-binding drugs and low-bindingdrugs; (3) calculating data values of the molecular descriptors, performing correlation screening, and selecting a group of molecular descriptors most related to a drug protein binding rate; (4) respectively establishing three levels of quantitative prediction models by adopting a machine learning algorithm; and (5) substituting the molecular descriptor of the drug molecule into the correspondinglevel of the quantitative prediction model, and predicting the protein binding rate of the drug molecule. According to the drug protein binding rate prediction method and system, the accuracy of PPBprediction of the highly bound drug can be improved, and the problem of low accuracy of PPB prediction of the highly bound drug in the prior art is solved.

Description

technical field [0001] The application belongs to the technical field of drug design, and specifically relates to the prediction of drug protein binding rate, in particular to a method and system for predicting drug protein binding rate based on structure and hierarchical classification. Background technique [0002] After the drug is absorbed into the blood from the administration site, part of it is combined with plasma proteins to become a bound drug, and part of it is in a free molecular state, and the drug can only be effective when it is in the free form [0003] The combination of drugs and plasma proteins not only affects the absorption, distribution, metabolism, and excretion of drugs in the body, but also is closely related to the intensity of pharmacological effects of drugs. Therefore, studying the plasma protein binding rate of drugs is not only beneficial to know the drug administration regimen design and safety evaluation, but also has great significance for t...

Claims

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Application Information

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IPC IPC(8): G16B15/30G16B20/00G16B40/00
CPCG16B15/30G16B20/00G16B40/00
Inventor 相小强袁雅文张政常硕张彦春李治纲蔡卫民田凌浩
Owner FUDAN UNIV
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