Many studies have indicated that computational approaches, such as predicting drug-target interaction networks [21] , prediction of body fluids [22] , predicting HIV cleavage sites in proteins [23, 24] , predicting protein metabolic stability [25] , predicting signal peptides [26] , identification of DNA Binding Proteins [27] , predicting the network of substrate-enzyme-product triads [28] , predicting protein subcellular locations [29, 30] , predicting proteases and their types [31] , predicting antimicrobial peptides [32] , predicting membrane proteins and their types [33] , predicting GPCRs and their types [34] , identifying nuclear receptor subfamilies [35] , predicting gram-negative bacterial protein cellular locations [36] , and predicting transcriptional activity of multiple site p53 mutants [37] , can provide many useful insights and data for which it would be time-consuming and costly to obtain by experiments alone. Actually, these data, combined with the information derived from the structural bioinformatics tools (see, e.g., [38] ), can timely provide very useful insights for both basic research and drug development. In view of this, the present study attempts to develop a new computational modeling method in the hopes it may become a useful tool for the drug development.