Application of Machine Learning Techniques in Drug-target Interactions Prediction

Author(s): Shengli Zhang*, Jiesheng Wang, Zhenhui Lin, Yunyun Liang

Journal Name: Current Pharmaceutical Design

Volume 27 , Issue 17 , 2021


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Abstract:

Background: Drug-Target interactions are vital for drug design and drug repositioning. However, traditional lab experiments are both expensive and time-consuming. Various computational methods which applied machine learning techniques performed efficiently and effectively in the field.

Results: The machine learning methods can be divided into three categories basically: Supervised methods, Semi-Supervised methods and Unsupervised methods. We reviewed recent representative methods applying machine learning techniques of each category in DTIs and summarized a brief list of databases frequently used in drug discovery. In addition, we compared the advantages and limitations of these methods in each category.

Conclusion: Every prediction model has both strengths and weaknesses and should be adopted in proper ways. Three major problems in DTIs prediction including the lack of nonreactive drug-target pairs data sets, over optimistic results due to the biases and the exploiting of regression models on DTIs prediction should be seriously considered.

Keywords: Drug-target interactions prediction, drug discovery, machine learning, computational methods, supervised learning, semisupervised learning, unsupervised learning.

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Article Details

VOLUME: 27
ISSUE: 17
Year: 2021
Published on: 24 November, 2020
Page: [2076 - 2087]
Pages: 12
DOI: 10.2174/1381612826666201125105730
Price: $65

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