Introduction and Objective:
Breast cancer ranks as the second-most prevalent cause of
death among women worldwide, with particularly elevated mortality rates in India. Breast cancer’s
origin involves biochemical pathway alterations influenced by tumor-inducing proteins. Research
has highlighted glycogen synthase kinase-3 beta (GSK-3β) as a crucial protein that regulates
the expression of various genes in the cell cycle. Mutations in this protein have a significant
impact on cellular development. As a consequence, it triggers aggressive subtypes of breast cancer,
such as triple-negative breast cancer. So, the primary aim of this study is to identify novel
chemicals targeting GSK-3β using machine learning methods, molecular modeling, and dynamic
techniques.
Materials and Methods:
To achieve the study's objective, small molecules were screened using a
Machine Learning (ML) approach, and subsequently, molecular docking and dynamic modelling
investigations were conducted to explore interactions between drugs and GSK-3β.
Results:
The research findings highlighted a specific compound, piperidine, 4-(3,4-
dichlorophenyl)-4-[4-(1H-pyrazol-4-yl) phenyl], which exhibited a superior docking score of -9.6
kcal/mol. Piperidine also formed conventional hydrogen bonds with the target protein. Furthermore,
the calculated binding free energy of -12.46 kcal/mol suggested that this compound exhibited
greater stability compared to commercially available drugs.
Conclusion:
These promising findings highlight the potential of piperidine and similar small
molecules as promising candidates for targeting the tumor-inducing protein GSK-3β. Subsequent
investigations, both in vitro and in vivo, will be essential to assess their effectiveness in combating
breast cancer.
