Structure-Based Molecular Simulation-Assisted Approaches for the Discovery of Anticancer Drugs Targeting the Epidermal Growth Factor Receptor

Abstract

Mutations in the epidermal growth factor receptor (EGFR) are major oncogenic drivers in several malignancies, particularly non-small cell lung cancer (NSCLC). The EGFR mutations play a significant role in treatment response and the development of drug resistance. Although successive generations of EGFR tyrosine kinase inhibitors (EGFR-TKIs) have substantially improved clinical outcomes, resistance-associated mutations such as T790M and C797S continue to limit treatment durability and create an urgent need for next-generation therapeutic strategies. Structure-based molecular simulation has emerged as a powerful approach for understanding mutation-dependent receptor behavior and guiding rational drug discovery. This review critically synthesizes current evidence on the application of structure-based computational approaches in EGFR-targeted anticancer drug development. By using a PRISMA-informed literature review, studies were evaluated according to methodological rigor, biological validation, and translational relevance. Furthermore, particular emphasis was placed on comparing strengths, limitations, and interpretive value of computational methods used in drug discovery research. The evidence indicates that molecular docking remains the most frequently applied screening method; however, its predictive value is limited when used alone. Studies integrating molecular docking with molecular dynamics simulation, free-energy calculations, and experimental validation consistently generated more reliable and translationally meaningful findings. Current research trends focus on developing mutant-selective inhibitors, dual-/multi-target compounds, allosteric inhibitors, natural product-derived drugs and repurposed drugs to address complex drug resistance mechanisms. In conclusion, this review highlights that structure-based molecular simulation has evolved beyond virtual screening into a mechanistic framework for understanding EGFR activation, therapeutic vulnerability, and resistance evolution. The findings suggest that integrated computational-experimental workflows are likely to play a central role in the development of next-generation EGFR-targeted therapies and the advancement of precision oncology for mutation-driven cancers.