  We present results of testing of the ability of eleven popular scoring
functions to predict native docked positions using a recently developed method
[1] for estimation the entropy contributions of relative motions to
protein-ligand binding affinity. The method is based on the integration of the
configurational integral over clusters obtained from multiple docked positions.
We use a test set of 100 PDB protein-ligand complexes and ensembles of 101
docked positions generated by Wang et al [2] for each ligand in the test set.
To test the suggested method we compare the averaged root-mean square
deviations (RMSD) of the top-scored ligand docked positions, accounting and not
accounting for entropy contributions, relative to the experimentally determined
positions. We demonstrate that the method increases docking accuracy by 10-21%
when used in conjunction with the AutoDock scoring function, by 2-25% with
G-Score, by 7-41% with D-Score, by 0-8% with LigScore, by 1-6% with PLP, by
0-12% with LUDI, by 2-8% with F-Score, by 7-29% with ChemScore, by 0-9% with
X-Score, by 2-19% with PMF, and by 1-7% with DrugScore. We also compare the
performance of the suggested method with the method based on ranking by cluster
occupancy only. We analyze how the choice of a RMSD-tolerance and a low bound
of dense clusters impacts on docking accuracy of the scoring methods. We derive
optimal intervals of the RMSD-tolerance for 11 scoring functions.
