QSAR, docking, ADMET, and system pharmacology studies on tormentic acid derivatives for anticancer activity.

To explore the anticancer compounds from tormentic acid derivatives, a quantitative structure-activity relationship (QSAR) model was developed by the multiple linear regression methods. The developed QSAR model yielded a high activity-descriptors relationship accuracy of 94% referred by regression coefficient (r2 = .94) and a high activity prediction accuracy of 91%. The QSAR study indicates that chemical descriptors, chiV5, T_T_Cl_7, T_2_T_4, SsCH3count, and Epsilon3 are significantly correlated with anticancer activity. This validated model was further been used for virtual screening and thus identification of new potential breast cancer inhibitors. Lipinski's rule of five, ADMET risk and synthetic accessibility are used to filter false positive hits. Filtered compounds were then docked to identify the possible target binding pocket, to obtain a set of aligned ligand poses and to prioritize the predicted active compounds. The scrutinized compounds, as well as their metabolites, were predicted and analyzed for different pharmacokinetics parameters such as absorption, distribution, metabolism, excretion, and toxicity. Finally, the top-ranked compound NB-12 was evaluated by system pharmacology approach. Later studied the metabolic networks, disease biomarker networks, pathway maps, drug-target networks and generate significant gene networks. The strategy applied in this research work may act as a framework for rational design of potential anticancer drugs.