Leveraging machine learning and molecular docking techniques to predict novel melanocortin-4 receptor agonists

Introduction : Monogenic obesity caused by mutations in the melanocortin-4 receptor (MC4R) gene remains a significant health challenge, despite numerous efforts to discover effective treatments. The MC4R has emerged as a promising target for drug development due to its role in energy homeostasis and adipose tissue formation. Aim : The present study explores the hybridization of machine learning and in silico techniques to propose natural compounds that potentially act as agonists against the obesity-related MC4R. Materials and methods : Specifically, a predictive model was developed to classify molecules based on their activity against the human MC4R (hMC4R). Additionally, a comprehensive molecular docking study was conducted on 2,000 compounds derived from natural sources to predict their binding affinity to the hMC4R. Results : The subsequent analysis of the docking results identified five natural compounds that have the potential to act as hMC4R agonists and contain a flavone chemical scaffold. Integration of the predictive model with molecular docking simulations reinforced these findings, illustrating the complementary roles of data-driven insights and structural assessments in pinpointing viable hit compounds. Conclusion : The study’s findings suggest that the flavone chemical scaffold could serve as a template for designing novel MC4R agonists.