A Review of Recent Advances and Research on Drug Target Identification Methods
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Published:2019-05-22
Issue:3
Volume:20
Page:209-216
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ISSN:1389-2002
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Container-title:Current Drug Metabolism
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language:en
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Short-container-title:CDM
Author:
Hu Yang1, Zhao Tianyi1, Zhang Ningyi1, Zhang Ying2, Cheng Liang3
Affiliation:
1. School of Life Science and Technology, Department of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China 2. Department of Pharmacy, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin 150088, China 3. College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
Abstract
Background:From a therapeutic viewpoint, understanding how drugs bind and regulate the functions of their target proteins to protect against disease is crucial. The identification of drug targets plays a significant role in drug discovery and studying the mechanisms of diseases. Therefore the development of methods to identify drug targets has become a popular issue.Methods:We systematically review the recent work on identifying drug targets from the view of data and method. We compiled several databases that collect data more comprehensively and introduced several commonly used databases. Then divided the methods into two categories: biological experiments and machine learning, each of which is subdivided into different subclasses and described in detail.Results:Machine learning algorithms are the majority of new methods. Generally, an optimal set of features is chosen to predict successful new drug targets with similar properties. The most widely used features include sequence properties, network topological features, structural properties, and subcellular locations. Since various machine learning methods exist, improving their performance requires combining a better subset of features and choosing the appropriate model for the various datasets involved.Conclusion:The application of experimental and computational methods in protein drug target identification has become increasingly popular in recent years. Current biological and computational methods still have many limitations due to unbalanced and incomplete datasets or imperfect feature selection methods
Funder
China Postdoctoral Science Foundation Heilongjiang Postdoctoral Fund National Natural Science Foundation of China Fundamental Research Funds for the Central Universities
Publisher
Bentham Science Publishers Ltd.
Subject
Clinical Biochemistry,Pharmacology
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