Due to time and cost limitations of experimental approaches, a number of predictive approaches attempt to predict target-ligand relationships in silico. The traditional computational predictive methods roughly fall into two categories: target-based approaches and ligand-based approaches [3]. Target-based approaches mainly utilize the target information to predict. Molecular docking is a target-based approach [4,5], which predicts the preferred orientation by conformation searching and energy minimization. Docking could provide excellent conformation, but it is difficult to find a rank/evaluation function to select which orientation is more appropriate [6]. Another target-based method is comparing target similarities, which compares the targets of a given ligand by sequences, EC number, domains, 3D structures, etc. Ligand-based methods compare candidate ligands with the known ligands of a given target to make a prediction [3]. Three-dimensional quantitative structure-activity relationship (3D-QSAR) is a typical ligand-based model [7], which indirectly reflect non-bonding interaction characteristics between the ligand and target. The most widely used 3D-QSAR methods are comparative molecular field analysis (CoMFA) and comparative molecular similarity (CoMSIA). CoMFA first aligns the ligands capable of binding to a given target, and then measure field intensities around the aligned ligands by different atom probes (force field-based). Finally, the measured field intensities are regressed with the active values and the regression equation is applied to predict interactions. Moreover, we can map the coefficients of CoMFA back into 3D space to obtain a 3D-QSAR model, which could guide the optimization of lead compounds [7].