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DTIP: A Comparative Analytical Framework for Chemogenomic Drugtarget Interactions Prediction

[ Vol. 17 , Issue. 1 ]


Faraneh Haddadi and Mohammad Reza Kayvanpour*   Pages 2 - 21 ( 20 )


Background: Prediction of drug-target interactions is an essential step in drug discovery. Given drug-target interactions network, the objective of this task is to predict probable missing edges from known interactions. Computationally predicting drug-target interactions is an appropriate alternative for the time-consuming and costly experimental process of drug-target interaction prediction. A large number of computational methods for solving this problem have been proposed in recent years.

Objective: In recent years, several review articles have been published in the field of drug-target interactions prediction. Compared to other review articles, this paper includes a qualitative analysis in the form of a framework, a drug-target interactions prediction (DTIP) framework.

Methods: The framework consists of three sections. Initially, a classification has been presented for drug-target interactions prediction methods based on the link prediction approaches used in these methods. Secondly, general evaluation criteria have been introduced for analyzing approaches. Finally, a qualitative comparison is made between each approach in terms of their advantages and disadvantages.

Results: By providing a new classification of the drug-target interactions prediction approaches and comparing them with the proposed evaluation criteria, this framework provides a convenient and efficient way to select and compare the methods. Moreover, using the framework, we can improve these techniques further.

Conclusion: This paper provides a study to select, compare, and improve chemogenomic drugtarget interactions prediction methods. To this aim, an analytical framework is presented.


Chemogenomic, drug-target interactions prediction, drug-target interactions network, machine learning, link prediction, comparative analytical framework, drug discovery.


Department of Computer Engineering and Data Mining Laboratory, Alzahra University, Vanak, Tehran, Department of Computer Engineering, Alzahra University, Vanak, Tehran

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