emergence of primateborne pathogens such as HIV/SIV and will form a scientific basis for guiding policies and programs to deter the spread of emerging zoonotic pathogens. Application of Consensus Scoring and Principal Component Analysis for Virtual Screening against β-Secretase (BACE-1) BACKGROUND: In order to identify novel chemical classes of β-secretase (BACE-1) inhibitors, an alternative scoring protocol, Principal Component Analysis (PCA), was proposed to summarize most of the information from the original scoring functions and re-rank the results from the virtual screening against BACE-1. METHOD: Given a training set (50 BACE-1 inhibitors and 9950 inactive diverse compounds), three rank-based virtual screening methods, individual scoring, conventional consensus scoring and PCA, were judged by the hit number in the top 1% of the ranked list. The docking poses were generated by Surflex, five scoring functions (Surflex_Score, D_Score, G_Score, ChemScore, and PMF_Score) were used for pose extraction. For each pose group, twelve scoring functions (Surflex_Score, D_Score, G_Score, ChemScore, PMF_Score, LigScore1, LigScore2, PLP1, PLP2, jain, Ludi_1, and Ludi_2) were used for the pose rank. For a test set, 113,228 chemical compounds (Sigma-Aldrich® corporate chemical directory) were docked by Surflex, then ranked by the same three ranking methods motioned above to select the potential active compounds for experimental test. RESULTS: For the training set, the PCA approach yielded consistently superior rankings compared to conventional consensus scoring and single scoring. For the test set, the top 20 compounds according to conventional consensus scoring were experimentally tested, no inhibitor was found. Then, we relied on PCA scoring protocol to test another different top 20 compounds and two low micromolar inhibitors (S450588 and 276065) were emerged through the BACE-1 fluorescence resonance energy transfer (FRET) assay. CONCLUSION: The PCA method extends the conventional consensus scoring in a quantitative statistical manner and would appear to have considerable potential for chemical screening applications. Molecular docking-based virtual screening is widely used to discover novel ligands in the early stages of drug development [1, 2, 3, 4] . Various docking programs, such as DOCK [5] , AutoDock [6] , Surflex [7] , FlexX [8] , GOLD [9] , and Glide [10, 11] , have been developed. As an essential component of these programs, the scoring function is able to evaluate the fitness between the ligand and receptor guiding the conformational and orientational search of ligand-binding poses. Since the 1990s, several dozens of scoring functions have been reported in the literature [12, 13] . Current scoring functions can be roughly classified as force-field-based methods [5, 14, 15] , empirical scoring functions [16, 17] , and knowledge-based statistical potentials [18] . The existing limitations in current docking and scoring include a lack of protein flexibility, inadequate treatment of solvation, and the simplistic nature of the energy function employed [19, 20, 21, 22] . In particular, the major weakness of docking programs lies in the scoring functions [12, 13] . Considering the computational cost and time required for virtual screening, all of the current scoring functions use various approximations resulting in inaccuracy in the score and rank of the ligand-binding poses [19] as well as in false positives mixed in with the top scorers in the ranking list when virtual screening was performed with only a single scoring function. Some studies focus on calculating proteinligand free binding energy, free energy perturbation (FEP), thermodynamic integration (TI) [23, 24, 25] , MM-PB/SA, MM-GB/SA [26, 27, 28] and linear interaction energy (LIE) [29, 30, 31] , which were used to perform post-docking processing. Although these methods are reported to be significantly more robust and more accurate than scoring functions, the accuracy is less than that usually required in typical lead optimization applications to differentiate highly similar compounds.