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 = 0.81 and q = 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 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.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10335751PMC
http://dx.doi.org/10.3389/fchem.2023.1197665DOI Listing

Publication Analysis

Top Keywords

imidazole derivatives
8
breast cancer
8
combination multi-approach
4
multi-approach studies
4
studies explore
4
explore potential
4
potential therapeutic
4
therapeutic mechanisms
4
mechanisms imidazole
4
derivatives mcf-7
4

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!