In Silico Investigation of New Drug Design and Development for the Treatment of Lung Cancer With Natural Quercetin and Its Derivatives

Abstract

Background: Developing new drugs is a time-consuming and risky process that requires significant financial investment, resources, and labor. To discover potent new drugs, the process must be performed step-by-step. Computational approaches are increasingly popular in drug discovery due to their ability to combine physical and biochemical processes, potentially increasing success rates and reducing costs. Aim of this study: Lung cancer is a significant global public health issue, and the need for new, effective, and safe treatments is critical. Quercetin, a natural compound, and its derivatives have demonstrated potent anticancer activity, making them an attractive option for lung cancer treatment. Method: This research utilized the latest computer-aided drug-design approaches, such as online data collection (Pass prediction, Lipinski rule, Pharmacokinetics, and Drug likeness, ADMET), structure optimization, and molecular docking, to identify new bioactive compounds for treating lung cancer. Findings: The study began by calculating the biological pass prediction spectrum to select the target protein, and cancerous proteins were identified through a high probability of active scores. Docking scores for lung cancer were -7.1 to -10.5 kcal/mol, and drug-likeness, ADME, and toxicity prediction were also evaluated. Conclusion: The results indicate that the natural quercetin and its derivatives have the potential as effective inhibitors for treating lung cancer due to their strong anticancer activity, good pharmacokinetic properties, and lack of toxicity. Keywords: Computational, Design, Quercetin, Lung cancer, Docking.