Implicated crime principle and network topological structural feature based recognition method for drug-target interaction

Abstract

The present invention discloses an implicated crime principle and network topological structural feature based recognition method for drug-target interaction. The method comprises: firstly, according to human protein-protein interaction data and drug-target interaction data, constructing a drug-target interaction group network which comprises a protein-protein interaction sub-network, a drug-target interaction sub-network and a drug-drug relationship sub-network; according to information of a protein primary structure descriptor, a fingerprint feature of drug molecules and the reliability of interaction, weighting nodes and edges in the network; proposing a new network topological structural feature for characterizing a drug-target interaction pair based on an implicated crime principle and a graph theory; and finally, constructing a model by using a random forest algorithm and predicting a potential drug-target interaction effect in a proteome scale. The method does not require information of three-dimensional structures and the like of protein and drug molecules, is simpler, quicker and more accurate, and has high potential for application to the fields of new drug research and development, pathological study and the like.

Description

technical field [0001] The invention belongs to the technical field of computer aided drug design. More specifically, it relates to a drug-target interaction identification method based on the principle of implicative crime and network topology features. Background technique [0002] The research and development of new drugs has always been a time-consuming and laborious process. It is estimated that it takes an average of billions of dollars and more than a decade to bring a new drug to market. In recent years, the success rate of new drug development has been declining, and one of the main reasons for this phenomenon is the lack of drug-target interaction information. Most drugs are biologically active small molecules, which mainly block abnormal biological processes through the interaction with target proteins. Therefore, identifying drug-target interactions has always been an important part of drug development. Identifying drug-target interactions can not only reduce ...

AI Technical Summary

Problems solved by technology

Using protein primary structure descriptors and drug molecular fingerprint descriptors to characterize drug-target interaction pairs is a simple method, through which drug-target interaction pairs can be represented as a high-dimensional feature vector, but this method does not Consider the integrity and robustness of biological networks

Therefore, in recent years, researchers have proposed a network-based drug-target interaction identification method, but this method only simulates the drug-target interaction as a bipartite graph, and does not take into account the interactions between proteins and proteins and between drugs. , and only consider the protein and drug as a simple point, ignoring the physical and chemical properties

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