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Optimization of Compound Ranking for Structure‐Based Virtual Ligand Screening Using an Established FRED –Surflex Consensus Approach
Author(s) -
Du Jiangfeng,
Bleylevens Ivo W. M.,
Bitorina Albert V.,
Wichapong Kanin,
Nicolaes Gerry A. F.
Publication year - 2014
Publication title -
chemical biology and drug design
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.59
H-Index - 77
eISSN - 1747-0285
pISSN - 1747-0277
DOI - 10.1111/cbdd.12202
Subject(s) - virtual screening , computer science , conformational isomerism , ranking (information retrieval) , conformational ensembles , docking (animal) , data mining , artificial intelligence , machine learning , drug discovery , chemistry , bioinformatics , molecular dynamics , biology , computational chemistry , medicine , nursing , organic chemistry , molecule
The use of multiple target conformers has been applied successfully in virtual screening campaigns; however, a study on how to best combine scores for multiple targets in a hierarchic method that combines rigid and flexible docking is not available. In this study, we used a data set of 59 479 compounds to screen multiple conformers of four distinct protein targets to obtain an adapted and optimized combination of an established hierarchic method that employs the programs FRED and Surflex. Our study was extended and verified by application of our protocol to ten different data sets from the directory of useful decoys ( DUD ). We quantitated overall method performance in ensemble docking and compared several consensus scoring methods to improve the enrichment during virtual ligand screening. We conclude that one of the methods used, which employs a consensus weighted scoring of multiple target conformers, performs consistently better than methods that do not include such consensus scoring. For optimal overall performance in ensemble docking, it is advisable to first calculate a consensus of FRED results and use this consensus as a sub‐data set for Surflex screening. Furthermore, we identified an optimal method for each of the chosen targets and propose how to optimize the enrichment for any target.