4.3 Review

A Review of Recent Advances and Research on Drug Target Identification Methods

期刊

CURRENT DRUG METABOLISM
卷 20, 期 3, 页码 209-216

出版社

BENTHAM SCIENCE PUBL LTD
DOI: 10.2174/1389200219666180925091851

关键词

Drug target; machine learning; biological experiment; protein structure; drug database; biological method

资金

  1. Fundamental Research Funds for the Central Universities [HIT NSRIF 201856]
  2. National Natural Science Foundation of China [61502125]
  3. Heilongjiang Postdoctoral Fund [LBH-Z6064, LBH-Z15179]
  4. China Postdoctoral Science Foundation [2016M590291]

向作者/读者索取更多资源

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.

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