Exploring innovative strategies for identifying anti-breast cancer compounds by integrating 2D/3D-QSAR, molecular docking analyses, ADMET predictions, molecular dynamics simulations, and MM-PBSA approaches

Breast cancer is a crucial global health issue, representing the most frequent cancer and a major cause of cancerrelated mortality of women. The difficulty of treating this disease is compounded by increasing drug resistance, which complicates both the efficacy of current therapies and the costs associated with developing new therapeutic agents. Computer-aided drug design (CADD) therefore appears to be a promising solution for reducing the cost and time needed to develop breast cancer drugs. In this research, we investigated 47 thiosemicarbazone, 1,2,4-triazole and thioether derivatives as potential therapeutic agents for breast cancer. Using 2D and 3D quantitative structure-activity relationship (QSAR) methodologies, we established models that were rigorously validated by internal and external procedures to improve their predictive capabilities for new breast cancer treatments. The reliability of the selected descriptors was corroborated by molecular docking studies targeting aromatase, a key enzyme in the pathology of the disease. ADMET property evaluations further corroborated the pharmaceutical potential of the newly designed ligands. Molecular dynamics and MM-PBSA simulations carried out a detailed analysis of the ligands' binding stability with the studied enzyme. Among the newly designed compounds, the ligand C2 has shown to be a promising hit for breast cancer inhibition. Further experimental validation in the future will be necessary to fully explore its therapeutic potential in vitro and in vivo.