The combination of multi-approach studies to explore the potential therapeutic mechanisms of imidazole derivatives as an MCF-7 inhibitor in therapeutic strategies

Breast cancer covers a large area of research because of its prevalence and high frequency all over the world. This study is based on drug discovery against breast cancer from a series of imidazole derivatives. A 3D-QSAR and activity atlas model was developed by exploring the dataset computationally, using the machine learning process of Flare. The dataset of compounds was divided into active and inactive compounds according to their biological and structural similarity with the reference drug. The obtained PLS regression model provided an acceptable r (2) = 0.81 and q(2) = 0.51. Protein-ligand interactions of active molecules were shown by molecular docking against six potential targets, namely, TTK, HER2, GR, NUDT5, MTHFS, and NQO2. Then, toxicity risk parameters were evaluated for hit compounds. Finally, after all these screening processes, compound C10 was recognized as the best-hit compound. This study identified a new inhibitor C10 against cancer and provided evidence-based knowledge to discover more analogs.

Automated summary

This study focused on the drug discovery against breast cancer using a series of imidazole derivatives and developed a 3D-QSAR and activity atlas model. The dataset of compounds was divided into active and inactive compounds based on their similarity to a reference drug. The obtained PLS regression model showed acceptable results with r(2) = 0.81 and q(2) = 0.51. Protein-ligand interactions of active molecules with six potential targets were investigated through molecular docking. Compound C10 was recognized as the best-hit compound after screening processes, and it showed potential as a new inhibitor against cancer.