Principal component analysis (PCA) can extract information from large-scale scoring data and decompose multiple scoring functions into one or two scoring functions, which can be used to re-score and re-rank the compound binding poses. As mentioned above, the PLP1&PLP2, ligscore1&ligscore2 and Ludi_1&Ludi_2 have high relative between each other. In addition, PMF_Score failed to rank any sensible docking poses. Thus, we do not use these four scoring functions in the following study. We constructed five 1000068 matrices using the remaining eight scoring functions as the matrix column and the 10000 compounds as the raws. Then the matrix was transformed such that each column of data had an average of zero and a standard deviation of one. As observed from Table 4 , we can derive eight uncorrelated descriptors (the principal components) from each scoring matrix. The weight of each principal component was determined based on their contribution rate to the variance (eigenvalues, l). We found that the first three principal components (PC1, PC2, PC3) account for .80% of the total variance for each pose group. The PC4, PC5, PC6, PC7, and PC8 could be omitted in further studies due to their trivial contribution to the total variance. This result is in agreement with the aim of introducing PCA to significantly minimize the number of variables and to omit the principle components with low variance that will not affect the total variance.