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1

Department of Biological Science, Purdue University, West Lafayette, IN 47907, USA

2

School of Life Science, Anhui University, Hefei 230601, China

3

Discovery Chemistry Research and Technologies, Eli Lilly and Company, Indianapolis, IN 46285, USA

4

Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA





*

Author to whom correspondence should be addressed.



Academic Editor: Derek J. McPhee

Abstract Virtual screening has been widely used in the drug discovery process. Ligand-based virtual screening LBVS methods compare a library of compounds with a known active ligand. Two notable advantages of LBVS methods are that they do not require structural information of a target receptor and that they are faster than structure-based methods. LBVS methods can be classified based on the complexity of ligand structure information utilized: one-dimensional 1D, two-dimensional 2D, and three-dimensional 3D. Unlike 1D and 2D methods, 3D methods can have enhanced performance since they treat the conformational flexibility of compounds. In this paper, a number of 3D methods will be reviewed. In addition, four representative 3D methods were benchmarked to understand their performance in virtual screening. Specifically, we tested overall performance in key aspects including the ability to find dissimilar active compounds, and computational speed. View Full-Text

Keywords: ligand-based virtual screening; three-dimensional similarity; ROCS; USR; 3D Zernike descriptors; Patch-Surfer; PL-PatchSurfer; molecular shape; molecular surface ligand-based virtual screening; three-dimensional similarity; ROCS; USR; 3D Zernike descriptors; Patch-Surfer; PL-PatchSurfer; molecular shape; molecular surface





Autor: Woong-Hee Shin 1, Xiaolei Zhu 2, Mark Gregory Bures 3 and Daisuke Kihara 1,4,*

Fuente: http://mdpi.com/



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