Herein, a combined molecular docking-based and pharmacophore-based target 
prediction strategy is presented, in which a probabilistic fusion method is 
suggested for target ranking. Establishment and validation of the combined 
strategy are described. A target database, termed TargetDB, was firstly 
constructed, which contains 1105 drug targets. Based on TargetDB, the molecular 
docking-based target prediction and pharmacophore-based target prediction 
protocols were established. A probabilistic fusion method was then developed by 
constructing probability assignment curves (PACs) against a set of selected 
targets. Finally the workflow for the combined molecular docking-based and 
pharmacophore-based target prediction strategy was established. Evaluations of 
the performance of the combined strategy were carried out against a set of 
structurally different single-target compounds and a well-known multi-target 
drug, 4H-tamoxifen, which results showed that the combined strategy consistently 
outperformed the sole use of docking-based and pharmacophore-based methods. 
Overall, this investigation provides a possible way for improving the accuracy 
of in silico target prediction and a method for target ranking.