In silico Modelling of 2D, 3D Molecular Descriptors for Prediction Of Anticancer Activities Of Luteolin And Daidzin From Plants Perilla ocymoides L and Glucine max L

Recently, we have isolated two flavonoids luteolin and daidzin from leaves of Perilla ocymoides L and Glucine max L in Viet Nam [1], with cytotoxic activity relatively strong in Hela cell line. To clarify the important nature of the relationships between structure and activity, the QSAR studies on Hela cell line incorporated the principal component analysis (PCA) technique and the artificial neural network (ANN) to construct the QSARPCA-ANN relationships. The best multiple linear model QSARMLR (with k = 6) values R2train of 0.854 and R2pred of 0.812, and QSARPCR (with k = 6) values R2train of 0.937 and R2pred of 0.889 were found by using the multiple linear regression technique. The artificial neural network QSARPCA-ANN with architectural style I (6)-HL (9)-O (1) represented the values R2train of 0.993 and R2pred of 0.971. In the case the incorporated model QSARPCA-ANN with the architecture I (6)-HL (9)-O (1) was exhibited the higher training and predicted quality. The anticancer activities of test substances resulting from those models are in good agreement with those from literature. The anti-cancer activities of two compounds luteolin and daidzin from leaves of Perilla ocymoides L and Glucine max L resulting from those models turn out to be agreement with experimental data.