Integrating QSAR modeling, ADMET screening, molecular docking, and molecular dynamics simulations to identify...

In this study, quantitative structure-activity relationship (QSAR) models were developed by the Monte Carlo technique to predict the anti-breast cancer activity of 144 novel 1,2-naphthoquinone and 1,4-naphthoquinone derivatives against MCF-7 breast cancer cells. To establish QSAR models, a balance of correlation techniques involving the index of ideality of correlation (IIC) and the correlation intensity index (CII), as well as an optimal hybrid descriptor derived from the integration of the Simplified Molecular Input Line Entry System (SMILES) and molecular hydrogen-suppressed graphs (HSG), was used. The resulting models provided valuable information about identifying molecular fragments that enhance or reduce biological activity. The pIC₅₀ values of 2435 naphthoquinone derivatives, including newly synthesized compounds, were predicted using the best QSAR model. Among them, 67 compounds showed pIC₅₀ values greater than 6. After applying the absorption, distribution, metabolism, excretion, and toxicity (ADMET) filter, 16 promising compounds were selected for docking studies. The candidate inhibitors were docked at the binding site of topoisomerase IIα (PDB ID: 1ZXM) to assess their binding affinity. Compound A14, which exhibited the highest binding affinity, underwent molecular dynamics simulations for 300 ns, demonstrating stable interactions with the target protein. Doxorubicin served as a reference control to validate the efficacy of compound A14. These findings offer valuable insights for designing potent inhibitors against breast cancer.