Abstract
Derivatives of 3-phenyl-2H-1,3-benzoxazine-2,4(3H)-dione (PBOD) have become important mainly as perspective antituberculotic drugs. Quantitative structure-retention relationship (QSRR) is used for predicting the HPLC retention factor of this group of compounds and optimal chromatographic conditions appropriate for this purpose are selected.
Among many molecular properties utilizable as the QSRR descriptors, mainly, in silico variables are advantageous as they closely characterize the HPLC retention of the PBOD molecule. Additionally, they are available without a need of the compound synthesis, which is important in the first stage of development of the potential drug.
Artificial neural networks (ANN) were successfully used as the basic modeling QSRR tools because their regression outputs allow a direct prediction of retention factors for different combinations of the stationary and mobile phases.