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Review | Open Access

Docking-based inverse virtual screening: methods, applications, and challenges

Xianjin Xu1,2,3,4Marshal Huang1,3Xiaoqin Zou1,2,3,4( )
Dalton Cardiovascular Research Center, University of Missouri, Columbia, MO 65211, USA
Department of Physics and Astronomy, University of Missouri, Columbia, MO 65211, USA
Informatics Institute, University of Missouri, Columbia, MO 65211, USA
Department of Biochemistry, University of Missouri, Columbia, MO 65211, USA
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Graphical Abstract


Identifying potential protein targets for a small-compound ligand query is crucial to the process of drug development. However, there are tens of thousands of proteins in human alone, and it is almost impossible to scan all the existing proteins for a query ligand using current experimental methods. Recently, a computational technology called docking-based inverse virtual screening (IVS) has attracted much attention. In docking-based IVS, a panel of proteins is screened by a molecular docking program to identify potential targets for a query ligand. Ever since the first paper describing a docking-based IVS program was published about a decade ago, the approach has been gradually improved and utilized for a variety of purposes in the field of drug discovery. In this article, the methods employed in docking-based IVS are reviewed in detail, including target databases, docking engines, and scoring function methodologies. Several web servers developed for non-expert users are also reviewed. Then, a number of applications are presented according to different research purposes, such as target identification, side effects/toxicity, drug repositioning, drug–target network development, and receptor design. The review concludes by discussing the challenges that docking-based IVS needs to overcome to become a robust tool for pharmaceutical engineering.



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Cite this article:
Xu X, Huang M, Zou X. Docking-based inverse virtual screening: methods, applications, and challenges. Biophysics Reports, 2018, 4(1): 1-16.











Received: 27 July 2017
Accepted: 08 September 2017
Published: 01 February 2018
© The Author(s) 2018

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