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Drug connection graph score prediction method and device based on double-graph convolution fusion model

A technology that integrates models and prediction methods, applied in the field of bioinformatics, can solve the problems of time-consuming and capital-consuming, low efficiency, etc., and achieve the effect of reducing time and capital costs

Pending Publication Date: 2021-11-09
WUHAN UNIV
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Problems solved by technology

[0007] The main purpose of the present invention is to provide a drug connection graph score prediction method and device based on a double-graph convolution fusion model, aiming to solve the problem of the need for experimental analysis in the prior art to obtain the connection graph score between drugs, which is time-consuming and financial and technical inefficiencies

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  • Drug connection graph score prediction method and device based on double-graph convolution fusion model
  • Drug connection graph score prediction method and device based on double-graph convolution fusion model
  • Drug connection graph score prediction method and device based on double-graph convolution fusion model

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[0051] It is to be understood that the specific embodiments described herein are intended to explain the present invention and is not intended to limit the invention.

[0052] A first aspect, the present invention provides a prediction method based on double convolution FIG pharmaceutical fusion model score connected FIG.

[0053] In one embodiment, reference figure 1 , figure 1 FIG convolution based on two connected FIG pharmaceutical fusion model prediction scores a schematic flow chart of a method according to an embodiment of the present invention. like figure 1 , The model-based fusion of drug-bis FIG convolution connected FIG fractional prediction method comprising:

[0054] Step S10, the trained network to build, train the network consists of an encoder and a decoder, a network layer, and FIG attention layer encoder convolutional integration by the notation FIG network layer, wherein configuration;

[0055] In this embodiment, the encoder is calculated by the trained networ...

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Abstract

The invention provides a drug connection graph score prediction method and device based on a double-graph convolution fusion model. The method includes: after a drug association graph is trained by using a symbol graph convolutional network layer, fusing node features containing global information into a drug molecular graph of each drug through transformation of a full connection layer, then training the drug molecular graph fused with the global features by using a graph attention network layer, carrying out pooling operation, acquiring fusion features of a drug, realizing communication fusion of global information and local information, decoding the fusion features by calculating cosine similarity, comparing a predicted value with a true value, calculating an error, and performing back propagation and continuous iteration to obtain a drug connection diagram score prediction network for predicting drug connection diagram scores of the drug pairs. According to the method and device, the connection diagram score of the drug pair can be predicted quickly and accurately, candidate drugs can be screened, and time and capital costs can be reduced.

Description

Technical field [0001] The present invention relates to the field of biological information technology, and more particularly to a pharmaceutical connection score prediction method and apparatus based on a double diagram convolutionary fusion model. Background technique [0002] The research of drug properties is one of the most important challenges of modern medicine. It is found that new drugs and new features of existing drugs have always been a hot problem in the field of pharmacology. However, since the compound is very complex, drug research usually requires a lot of time and money. [0003] When the researcher is trying to find new compounds with certain biological activity, the high-flux screening technology (HTS) is first used, and since HTS uses violent exhaustion methods to screen a large number of candidate compounds in huge search space, this step is very time consuming. And cost is high. The next step is to optimize candidate compounds to achieve the desired biologi...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16C20/50G06N3/04G06N3/08
CPCG16C20/50G06N3/084G06N3/045
Inventor 洪程之章文刘峰
Owner WUHAN UNIV
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