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Application of multivariate data analysis has become a popular method in the last decades, mainly because it can provide information not otherwise accessible. The information includes classification, searching similarities, finding relationships, finding physical significance to principal components, etc. Twenty-two Chinese medicinal herbs containing twelve constituents were collected and determined by HPLC. The results were studied by hierarchical cluster analysis (HCA) and principal components analysis (PCA). It was shown that the samples could be clustered reasonably into three groups, hence corresponding with the typical habitats of Psoralea corylifolia L.

Molecular similarity method was applied to the study of quantitative structure-retention relationship (QSRR) for reversed-phase high performance liquid chromatography (RP-HPLC) analysis of drugs. Based on a thorough and systematic study on the molecular structures of 162 drugs, molecular similarity method, which transformed molecular structure parameters to similarity variables, combined with artificial neural network for the study of QSRR for RP-HPLC. The good relationship module reflecting molecular structure, solvent strength and capacity factor was established. Molecular similarity method was successfully used to predict capacity factors (k') and the validation results of 7 drugs were satisfactory. The correlation coefficient of test samples was 0.996, and the residual standard error was 0.244(n = 18). The correlation coefficient of validation samples was 0.992, and the residual standard error was 0.131(n = 7). The application of molecular similarity method in the study of QSRR for RP-HPLC of drugs is satisfactory.