First, PCA is efficient. For each five pose group, the application of PCA can result in superior enrichment of known inhibitors compared to either the conventional consensus scoring or the best individual scoring. In addition, the application of PCA for postprocessing of the scoring data from virtual screening was not timeconsuming. Second, PCA is reliable. PCA is mainly useful when there is limited knowledge about the target and its inhibitors. If we have no idea which scoring function would return the best enrichment rates (i.e., several known active compounds are required to determine the best scoring functions), then adopting PCA to formulate a new scoring function can provide better performance than blindly using a scoring function or some combination scoring functions when performing virtual screening 50 , and training with a data set were not necessary. When a training set was available, there are several other groups that perform a different type of post-docking processing using statistical methods and data mining. Wilton et al. discussed the use of several rank-based virtual screening methods, such as binary kernel discrimination, similarity searching, sub-structural analysis, support vector machine (SVM), and trend vector analysis, for prioritizing compounds in lead-discovery programs [76, 77] . Jacobsson et al. employed three different multivariate statistical methods including PLS discriminant analysis, rule-based methods, and Bayesian classification to analyze multidimensional scoring data from four different target proteins (i.e., the estrogen receptor R (ERR), matrix metalloprotease 3 (MMP3), factor Xa (fXa), and acetylcholine esterase (AChE)). The classifiers that they built showed that the precision is approximately 90% for three of the targets and approximately 25% for acetylcholine esterase for correctly predicting an active compound [78] . The difference between our work and their's is that we do not need a training set because PCA is a form of unsupervised learning and relies entirely on the input data itself. In addition, PCA is simpler than the methods mentioned above (SVM, trend vector analysis, PLS discriminant analysis, rule-based methods, and Bayesian classification).