Structure-based virtual screening relies on scoring the predicted binding modes 
of compounds docked into the target. Because the accuracy of this scoring relies 
on the accuracy of the docking, methods that increase docking accuracy are 
valuable. Here, we present a relatively straightforward method for improving the 
probability of identifying accurately docked poses. The method is similar in 
concept to consensus scoring schemes, which have been shown to increase ranking 
power and thus hit rates, but combines information about predicted binding modes 
rather than predicted binding affinities. The pose prediction success rate of 
each docking program alone was found in this trial to be 55% for Autodock, 58% 
for DOCK, and 64% for Vina. By using more than one docking program to predict 
the binding pose, correct poses were identified in 82% or more of cases, a 
significant improvement. In a virtual screen, these more reliably posed 
compounds can be preferentially advanced to subsequent scoring stages to improve 
hit rates. Consensus docking can be easily introduced into established 
structure-based virtual screening methodologies.