Wen Zhang*, Weiran Lin, Ding Zhang, Siman Wang, Jingwen Shi and Yanqing Niu Pages 194 - 202 ( 9 )
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.
Machine learning, drug-target interaction, drug discovery, drug repurposing, molecular fingerprint, similarity measure.
School of Computer Science, Wuhan University, Wuhan 430072, School of Computer Science, Wuhan University, Wuhan 430072, School of Computer Science, Wuhan University, Wuhan 430072, School of Computer Science, Wuhan University, Wuhan 430072, School of Mathematics and Statistics, Wuhan University, Wuhan 430072, School of Mathematics and Statistics, South-Central University for Nationalities, Wuhan 430074