Target prediction method

Abstract

The embodiment of the invention aims to provide a drug-target relation prediction method, and aims to solve the problems that in the prior art, target prediction by using machine learning is not high in accuracy, target prediction based on a graph model is poor in unknown drug-target relation prediction, and the prediction time is long. The invention provides a new similarity matrix of drugs, and between drugs and targets, and the similarity matrix is used to predict the relationship between the drugs and the targets. By utilizing the method, the target prediction accuracy can be improved, the time required for target prediction can be shortened, and the method has important significance on development of new drugs.

Description

technical field [0001] The invention relates to computational biology, in particular to a drug-target relationship prediction method. Background technique [0002] According to statistics, a new drug costs as much as 200-350 million US dollars from screening to successful marketing, and it takes an average of 10 to 14 years. Currently, the elimination rate of drug candidates in the clinical stage is as high as 90%. Among them, the discovery and confirmation of drug targets is a particularly complicated process, and confirmation of a simple drug target requires a huge cost of money and time. Therefore, how to reduce the cost of the process from discovery of drug targets to successful drug launch (R&D) and increase the speed of drug target discovery to clinical research has become the research focus of major pharmaceutical companies and academic institutions. [0003] An important link in the drug research process is the screening of new drugs, and the key to establishing a ...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the embodiments of the present invention is to provide a drug-target relationship prediction method, which aims to solve the problem that the target prediction algorithm based on the traditional bipartite graph model is poor in predicting the unknown drug-target relationship, while the traditional logic matrix decomposition The problem that the target prediction algorithm takes a long time to predict

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