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
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.