The generated quantitative pharmacophore model was validated to find out whether it is competent enough to identify the active structures and estimate their activity values precisely. This validation process was performed based on test set prediction and Fischer randomization methods. In developing statistical models, the following three cross-validation methods are often used to examine a model or predictor for its effectiveness in practical application: independent dataset test, subsampling test, and jackknife test [69] . However, of the three test methods, the jackknife test is deemed the most objective [29] . The reasons are as follows. (i) For the independent dataset test, although all the samples used to test the model or predictor are outside the training dataset used to train it so as to exclude the "memory" effect or bias, the way of how to select the independent samples to test the model or predictor could be quite arbitrary unless the number of independent proteins is sufficiently large. This kind of arbitrariness might result in completely different conclusions. For instance, a model or predictor achieving a higher success rate than the other model or predictor for a given independent testing dataset might fail to keep so when tested by another independent testing dataset [69] ; (ii) For the subsampling test, the concrete procedure usually used in literatures is the 5-fold, 7-fold or 10-fold cross-validation. The problem with this kind of subsampling test is that the number of possible selections in dividing a benchmark dataset is an astronomical figure even for a very simple dataset, as elucidated demonstrated by Equations 28-30 in [70] . Therefore, in any actual subsampling cross-validation tests, only an extremely small fraction of the possible selections are taken into account. Since different selections will always lead to different results even for a same benchmark dataset and a same model or predictor, the subsampling test cannot avoid the arbitrariness either. A test method unable to yield a unique outcome cannot be deemed as a good one; (iii) In the jackknife test, all the samples in the benchmark dataset will be singled out one-by-one and tested by the model or predictor trained by the remaining samples. During the process of jackknifing, both the training dataset and testing dataset are actually open, and each sample will be in turn moved between the two. The jackknife test can exclude the "memory" effect. Also, the arbitrariness problem as mentioned above for the independent dataset test and subsampling test can be avoided because the outcome obtained by the jackknife cross-validation is always unique for a given benchmark dataset. Accordingly, the jackknife test has been increasingly and widely used by those investigators with strong math background to examine the quality of various predictors (see e.g., [30, [71] [72] [73] [74] [75] ). However, to reduce the computational time, we adopted the independent testing dataset cross-validation in this study as done by many investigators with SVM as the prediction engine. A test set comprising 97 compounds with experimentally known chymase inhibitory activity values was used in test set prediction method. Ligand Pharmacophore Mapping protocol running with BEST/Flexible conformation generation option was used to map the test set compounds. Fischer randomization method as available in DS was applied on training set compounds to prove that the generated pharmacophore model was not obtained by chance. A pharmacophore is only useful as a predictive model in finding novel, potential leads suitable for further development only if it is able to detect the compounds with known inhibitory activity. In order to identify new potential lead compounds, the selected pharmacophore model was subsequently used as 3D structural search query to screen the Maybridge and Chembridge chemical databases consisting of 60,000 and 50,000 of structurally assorted compounds, respectively. All queries were performed using Ligand Pharmacophore Mapping protocol running with Best/Flexible search method in DS. To be retrieved as a hit, a molecule must fit all the features of the pharmacophore hypothesis. The hits obtained through database screening were further filtered using Lipinsiki's rule of five in order to carry only drug-like compounds in further studies.