The performance of all four GOLD scoring functions has been evaluated for pose prediction and virtual screening under the standardized conditions of the comparative docking and scoring experiment reported in this Edition. Excellent pose prediction and good virtual screening performance was demonstrated using unmodified protein models and default parameter settings. The best performing scoring function for both pose prediction and virtual screening was demonstrated to be the recently introduced scoring function ChemPLP. We conclude that existing docking programs already perform close to optimally in the cognate pose prediction experiments currently carried out and that more stringent pose prediction tests should be used in the future. These should employ cross-docking sets. Evaluation of virtual screening performance remains problematic and much remains to be done to improve the usefulness of publically available active and decoy sets for virtual screening. Finally we suggest that, for certain target/scoring function combinations, good enrichment may sometimes be a consequence of 2D property recognition rather than a modelling of the correct 3D interactions.
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Cambridge Crystallographic Data Centre
Protein Data Bank
Root mean square deviation
Receiver operating characteristic
Area under curve
Directory of useful decoys
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This work was performed using the Darwin Supercomputer of the University of Cambridge High Performance Computing Service (http://www.hpc.cam.ac.uk/), provided by Dell Inc. using Strategic Research Infrastructure Funding from the Higher Education Funding Council for England. We also thank Dr Colin Groom for valuable comments regarding the manuscript.
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Liebeschuetz, J.W., Cole, J.C. & Korb, O. Pose prediction and virtual screening performance of GOLD scoring functions in a standardized test. J Comput Aided Mol Des 26, 737–748 (2012). https://doi.org/10.1007/s10822-012-9551-4
- Virtual screening
- Scoring function