Graph-Transform-based drug target interaction prediction method research

A prediction method and drug technology, applied in the field of biochemistry, can solve the problems of not considering the graph Transformer, not applying the graph structure, etc.

Pending Publication Date: 2022-01-28
CHANGCHUN UNIV OF TECH
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Problems solved by technology

However, most computational methods do not take into account the graph Transformer
The Transformer model fully realizes the explicit information...

Method used

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  • Graph-Transform-based drug target interaction prediction method research
  • Graph-Transform-based drug target interaction prediction method research
  • Graph-Transform-based drug target interaction prediction method research

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example 1

[0030] Example 1 The present invention is tested by the long text news collected by oneself

[0031] This dataset is a dataset consisting of 12,015 nodes and 1,895,445 edges, and is a dataset for drug-target interaction prediction tasks. The dataset integrates four types of nodes (drug, protein, disease, and side effect) and five types of edges (drug-drug interaction, drug-disease association, drug-side effect association, protein-disease association, and protein-protein interaction). The present invention selects the DTI-GTN model as the basic model of the drug target prediction model, and uses two indicators to evaluate its performance, namely Receiver Operating Characteristic Curve (ROC) and Precision-Recall (PR). For comparison, they are DTI-GTN, DTI-CNN, DTI-TAG, DTI-RGG, DTI-Hyper, DTI-RF, DTI-KNN. The existing 6 methods all run under their optimal parameters. The relevant parameters of the method of the present invention are set as follows: the number of epochs is 20...

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Abstract

The invention discloses a graph-Transform-based drug target interaction prediction method research, which aims to predict whether drug target pairs interact or not, and the method mainly comprises the steps of node feature processing, graph structure processing, model definition, model training and model evaluation. Rapid and accurate discovery of drugs capable of effectively treating diseases is very important for social development, but based on prediction among drug targets, the relationship among the drug targets cannot be completely obtained, information of some surrounding nodes is ignored, some current models cannot well extract features among global nodes, and based on the above consideration, the invention provides a drug target interaction prediction method based on a graph Transform. Whether drug target pairs interact or not can be automatically recognized.

Description

technical field [0001] The invention belongs to the field of biochemistry, and is a research on a drug target interaction prediction method based on graph Transformer. Background technique [0002] Today, tens of thousands of diseases are known to threaten human health, and new diseases are added every year. Therefore, it is very important for the development of society to quickly and accurately discover drugs that can effectively treat diseases. Drug-target prediction is to identify new ligands for new drugs and protein targets by identifying the interaction between drug compounds and protein targets, which can greatly reduce the cost and time of experiments. Traditional DTIs are roughly divided into two categories: one is based on molecular docking methods, and the other is based on ligand methods. These methods have obvious shortcomings. For example, the molecular docking method depends on the three-dimensional structure of the target protein. In reality, the three-dimen...

Claims

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Application Information

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IPC IPC(8): G16B15/30G06K9/62G06V10/774G06N3/04G06N3/08
CPCG16B15/30G06N3/084G06N3/045G06F18/214
Inventor 王红梅郭放张丽杰党源源
Owner CHANGCHUN UNIV OF TECH
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