The Computational Models of Drug-target Interaction Prediction

Author(s): Yijie Ding, Jijun Tang, Fei Guo*.

Journal Name: Protein & Peptide Letters

Volume 27 , Issue 5 , 2020

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Abstract:

The identification of Drug-Target Interactions (DTIs) is an important process in drug discovery and medical research. However, the tradition experimental methods for DTIs identification are still time consuming, extremely expensive and challenging. 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 semisupervised models, 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).

Keywords: Drug discovery, drug-target interaction, bipartite network, network analysis, machine learning, computational methods.

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Article Details

VOLUME: 27
ISSUE: 5
Year: 2020
Page: [348 - 358]
Pages: 11
DOI: 10.2174/0929866526666190410124110
Price: $65

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