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-target interaction, drug discovery, drug repurposing, molecular fingerprint, similarity measure.
Graphical Abstract
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