Computational Insights into the Anticancer Potential of Flavonoids isolated from Calotropis procera (Aiton)

Artificial intelligence (AI) is revolutionizing sustainable chemistry that enabling the efficient discovery and analysis of bioactive compounds through computational or bioinformatics method. This study investigates flavonoid compounds isolated from Calotropis procera using AI-driven molecular docking to determine its potential as an anticancer agent against EGFR Kinase domain (PDB ID: 3POZ) which inhibition will lead to the creation of new and novel drugs for cancer treatment with enhanced precision and potency. Flavonoids are a type of polyphenol found in fruits, vegetables and drinks. It possesses potential health benefits, particularly in preventing cancer and reducing oxidative stress. Advanced docking simulations were performed using Schrödinger\'s Maestro suite, which included protein and ligand preparation, receptor binding site was localized to the co crystalized native ligand (03P) and docking was performed in standard precision mode. The flavonoid compounds showed significant binding affinity with docking scores ranging from -8.686 to -5.522 kcal/mol. The native ligand (03P) achieved the highest docking score of -12.803 kcal/mol, indicating the strongest binding affinity. Among the isolated flavonoids: The best performing compound Isorhamnetin achieved a value of -8.686 kcal/mol while other compounds achieved values between -8.652 and -5.522 kcal/mol, reflecting moderate to strong affinities. These results highlight the promise of AI-driven approaches in sustainable drug discovery that optimize resource utilization while minimizing experimental waste.