In this study, we present an alternative method, principal component analysis (PCA) [35, 36, 37] , for performing a linear combination of multiple scoring functions, formulating a modified ranking score and PCscore, and re-scoring and re-ranking the compounds after virtual screening. PCA is a powerful tool for pattern recognition, classification, modeling, and other aspects of data evaluation [36] . In addition, PCA is a linear transformation technique used to simplify a data set by reducing the dimensionality of multivariate data while preserving as much of the relevant information as possible. The principal components (PCs) are linear combinations of the original variables. The linear coefficients of the inverse relationships of linear combinations are called the component loadings. It represents the correlation coefficients between the original variables and the PC. In the present study, the first principal component (PC1) accounts for the maximum variance (eigenvalue) in the original dataset. The second principal component (PC2) is orthogonal (uncorrelated) to the first one, and it accounts for most of the remaining variance. This procedure is continued until the total variance is accounted for. The method of PCA makes use of intercorrelations that originate from the covariance matrix of the variables.