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    Identifying drug-target interactions through computational methods is raised an important and key step in the process of drug discovery and drug-oriented research during the last years. In addition to the advantages of existing computational methods, there are also challenges that affect methods' efficiency and provide obstacles in the direction of developing these computational methods. However, the literature suffers from lacking a comprehensive and comparative analysis concerning drug-target interactions prediction (DTIP) focusing on the analysis of technical and challenging aspects. It seems necessary to provide a comparative perspective and a different analysis on a macro level due to the importance of the DTIP problem. In this paper, we presented the quadruple framework of analytical, named DTIP-TC2A consists of four main components for DTIP. The first component, categorizing DTIP methods based on the technical aspect ahead and investigating the strengths and weaknesses of different DTIP methods. Second, classify DTIP challenges with a major focus on a well-organized and coherent investigation of challenges and presenting a macro view of the DTIP challenges by systematic identification of them. Third, recommending some general criteria to analyze DTIP methods in form of the proposed classifications. Suggesting a suitable set of qualitative criteria along with using quantitative criteria can lead to a more proper choice of DTIP methods. Fourth, performing a two-phase qualitative analysis and comparison between each class of DTIP approaches based on the proposed functional criteria and the identified challenges ahead in order to understand the superiority of each class of DTIP methods over the other class. We believed that the DTIP-TC2A framework can offer a proper context for efficient selection of DTIP methods, improving the efficiency of a DTIP system due to the nature of computational methods, upgrading DTIP methods by removing the barriers, and presenting new directions of research for further studies through systematic identification of DTIP challenges and purposeful evaluation of challenges and methods.Copyright © 2022 Elsevier Ltd. All rights reserved.


    Mohammad Reza Keyvanpour, Faraneh Haddadi, Soheila Mehrmolaei. DTIP-TC2A: An analytical framework for drug-target interactions prediction methods. Computational biology and chemistry. 2022 May 31;99:107707

    PMID: 35691227

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