Molecular docking is the most commonly used technique in the modern drug 
discovery process where computational approaches involving docking algorithms 
are used to dock small molecules into macromolecular target structures. Over the 
recent years several evaluation studies have been reported by independent 
scientists comparing the performance of the docking programs by using default 
'black box' protocols supplied by the software companies. Such studies have to 
be considered carefully as the docking programs can be tweaked towards optimum 
performance by selecting the parameters suitable for the target of interest. In 
this study we address the problem of selecting an appropriate docking and 
scoring function combination (88 docking algorithm-scoring functions) for 
substrate specificity predictions for feruloyl esterases, an industrially 
relevant enzyme family. We also propose the 'Key Interaction Score System' 
(KISS), a more biochemically meaningful measure for evaluation of docking 
programs based on pose prediction accuracy.