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The Computational Models of Drug-target Interaction Prediction


Yijie Ding, Jijun Tang and Fei Guo*   Pages 1 - 11 ( 11 )


The identification of Drug-Target Interactions (DTIs) is an important process in drug discovery and medicine research. However, the tradition experimental methods for DTIs identification are still time consuming, extremely expensive and challenging, even now. In the past ten years, various computational methods have been developed to identify potential DTIs. In this paper, the identification methods of DTIs are summarized. What's more, several state-of-the-art computational methods are mainly introduced, containing network-based method and machine learning-based method. In particular, for machine learning-based methods, including the supervised and semi-supervised models, they have essential differences in the approach of negative samples. Although these effective computational models in identification of DTIs have achieved significant improvements, network-based and machine learning-based methods have their disadvantages, respectively. These computational methods are evaluated on four benchmark data sets via values of Area Under the Precision Recall curve (AUPR).


drug discovery, drug-target interaction, bipartite network, network analysis, machine learning.


School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, Department of Computer Science and Engineering, University of South Carolina, Columbia, School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin

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