Drug relocation method based on machine learning

A machine learning and relocation technology, applied in the field of machine learning, can solve problems such as time-consuming and long drug development cycle, and achieve the effect of improving efficiency, saving experimental costs, and avoiding risks.

Pending Publication Date: 2021-05-25
NORTHEASTERN UNIV
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AI Technical Summary

Problems solved by technology

[0005] Drug repositioning has the characteristics of high efficiency and low cost. Since the outbreak of the new crown epidemic, how to screen out new crown drugs has become an

Method used

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  • Drug relocation method based on machine learning
  • Drug relocation method based on machine learning
  • Drug relocation method based on machine learning

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Embodiment Construction

[0020] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0021] In this embodiment, the Windows system is used as the development environment, Jupyter Notebook is used as the development tool, Python is used as the development language, and the drug repositioning method based on machine learning of the present invention is used to reposition the medicine for treating diabetes.

[0022] In this embodiment, a drug repositioning method based on machine learning, such as figure 1 and figure 2 shown, including the following steps:

[0023] Step 1: According to the data set provided by the paper "Modeling polypharmacy side effects with graph convolutional networks." published by Stanford University, select 1,250 drugs as research o...

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Abstract

The invention provides a drug relocation method based on machine learning, and relates to the technical field of machine learning. The method comprises the following steps: selecting a plurality of medicines as samples, and obtaining indications of each medicine; selecting multiple target protein data as drug sample features, and performing data dimension reduction on the drug-target protein vector by using a data dimension reduction algorithm based on machine learning; selecting a plurality of physicochemical characteristics of each medicine by using a correlation analysis algorithm; by taking the dimension-reduced drug-target protein vector features and drug physicochemical features as features of drug molecules and indications of drugs as labels, constructing a drug curative effect data set, establishing three gradient boosting trees, and training the three gradient boosting trees by using data in the drug curative effect data set. According to the invention, the three boosting trees are fused to establish a drug curative effect prediction model, a Kflood algorithm is utilized to perform multiple rounds of prediction on the curative effect of the N drugs, and finally m drugs effective for treating a certain disease are predicted.

Description

technical field [0001] The present invention relates to the technical field of machine learning, in particular to a drug repositioning method based on machine learning. Background technique [0002] According to statistics, it takes about 15 years for a new drug to be conceived, synthesized as a lead compound, passed through clinical trials, and finally successfully marketed, costing about US$1 billion. And this cost is increasing year by year. Moreover, there are risks in drug research and development. If problems with the drug are found in the later stage of research and development, the previous investment will be wasted, so the risk is very high. [0003] Drug repositioning refers to the discovery of new indications for marketed drugs, and is an important application field of network pharmacology. The drug repositioning strategy is one of the strategies with the best risk-to-benefit ratio in the currently known drug development strategies, and it is also one of the eff...

Claims

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Application Information

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IPC IPC(8): G16B15/30G06K9/62G06N20/00
CPCG16B15/30G06N20/00G06F18/2135
Inventor 石阳任涛王逸群曲颖
Owner NORTHEASTERN UNIV
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