Computational Design of Pyrrolo[2,1‐f]triazine‐Based Dual Wild‐Type/Mutant <scp>EGFR</scp> Inhibitors: Pharmacophore Modelling, <scp>3D</scp> ‐ <scp>QSAR</scp> , Quantum Chemistry, and Post‐ <scp>MD</scp> Studies for Breast Cancer Therapeutics

ABSTRACT The global cancer burden data 2022 estimated 2.3 M newly diagnosed breast cancer cases with over 6 M mortalities. Moreover, the cases are expected to increase by 10% by 2045, raising health concerns worldwide. Triple‐negative breast cancer is a highly aggressive subtype of BC with a shorter survival rate and a higher likelihood of recurrence. EGFR overexpression is seen in 15%–45% of BC. It is associated with nearly 50% of TNBC and inflammatory breast cancer incidences, associated with motility, survival, and differentiation through mutation. This study investigates pyrrolo[2,1‐f][1,2,4]triazine analogs as potential inhibitors of both wild‐type and mutant (T790M) EGFR, utilizing computational approaches to address resistance and enhance breast cancer therapeutics. A dataset of pyrrolo[2,1‐f][1,2,4]triazine analogs was used to develop pharmacophore models and a predictive 3D‐QSAR model. The designed library underwent ADME screening, molecular docking with mutant and wild‐type EGFR, MM‐GBSA calculations, DFT, MD simulations, and post‐MD studies using Schrödinger 2025–2 software. Compound SK31 showed favorable docking (wild‐type: −10.8 kcal/mol; mutant: −9.4 kcal/mol), strong MM‐GBSA binding energies, and stable interactions in MD. DFT confirmed favorable electronic behavior. Post‐MD analyses revealed low‐energy conformational stability and strong residue correlations. This study provides a framework for designing pyrrolo[2,1‐f][1,2,4]triazine‐based EGFR inhibitors and offers valuable insights into future breast cancer therapeutic development.