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Curr Drug Metab. 2019;20(3):194-202. doi: 10.2174/1389200219666180821094047.

Recent Advances in the Machine Learning-Based Drug-Target Interaction Prediction.

Author information

1
School of Computer Science, Wuhan University, Wuhan 430072, China.
2
School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China.
3
School of Mathematics and Statistics, South-Central University for Nationalities, Wuhan 430074, China.

Abstract

BACKGROUND:

The identification of drug-target interactions is a crucial issue in drug discovery. In recent years, researchers have made great efforts on the drug-target interaction predictions, and developed databases, software and computational methods.

RESULTS:

In the paper, we review the recent advances in machine learning-based drug-target interaction prediction. First, we briefly introduce the datasets and data, and summarize features for drugs and targets which can be extracted from different data. Since drug-drug similarity and target-target similarity are important for many machine learning prediction models, we introduce how to calculate similarities based on data or features. Different machine learningbased drug-target interaction prediction methods can be proposed by using different features or information. Thus, we summarize, analyze and compare different machine learning-based prediction methods.

CONCLUSION:

This study provides the guide to the development of computational methods for the drug-target interaction prediction.

KEYWORDS:

Machine learning; drug discovery; drug repurposing; drug-target interaction; molecular fingerprint; similarity measure.

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