The computational models of drug-target interaction prediction.

The computational models of drug-target interaction prediction.

Publication date: Apr 10, 2019

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).

Ding, Y., Tang, J., and Guo, F. The computational models of drug-target interaction prediction. 04400. 2019 Protein Pept Lett.

Concepts Keywords
Drug Target Macroeconomic forecasting
Macroeconomic model
Improvements network

Semantics

Type Source Name
gene UNIPROT ARTN
gene UNIPROT AGRP
drug DRUGBANK Pentaerythritol tetranitrate
gene UNIPROT EHD1

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