Estimating the EGFR Ligand‐Binding Affinity and Pose via Molecular Docking Simulations

ABSTRACT Lung cancer, particularly non‐small cell lung cancer (NSCLC), remains the leading cause of cancer‐related mortality worldwide, with the epidermal growth factor receptor (EGFR) kinase having been identified as a critical therapeutic target. Therefore, the search for potential inhibitors for EGFR is an urgent task in treating NSCLC. In this context, we have benchmarked the performance of four popular molecular docking packages utilizing various scoring functions, including Ad4, Vina, Vinardo, and mVina. In particular, the metrics for ranking ligand‐binding affinity and successful‐docking rate were carefully computed. The performance of docking simulations against EGFR generated by SWISS‐MODEL and Robetta web servers was also assessed. The MD simulations were also carried out to provide ensembles of EGFR conformations to be used as receptor structures for molecular docking simulations. The performance of the docking program on these structures was also validated. Overall, it was demonstrated that the ad4 scoring function dominated over the others in accelerating the discovery of potent EGFR inhibitors for NSCLC treatment. This optimized scheme adopts an appropriate computational protocol to rapidly determine the lead compounds for NSCLC patients by inhibiting EGFR mutations.