Comparative Analysis of Molecular Docking Programs GOLD, Glide, and MOE on Quinazoline Derivatives as Antiproliferative Agents: Implications for EGFR-Targeted Therapies
Year: 2025
Type: article
Abstract: Background: To aid in the identification of drug candidates and the prediction of protein-ligand interactions, molecular docking predicts how ligands interact with receptors, aiding drug discovery. EGFR’s role in NSCLC makes quinazoline derivatives promising anticancer agents. However, accurately predicting their EGFR binding affinities remains a challenge. Objectives: This research was designed to comparatively analyze three popular molecular docking tools; GOLD, Glide, and MOE by comparing their performance in predicting quinazoline derivative binding poses and affinities against EGFR. This study aimed to identify the most effective docking tool for screening quinazoline-based antiproliferative drugs. Method: Quinazoline derivatives were docked into the EGFR receptor binding site using GOLD, Glide, and MOE. The ligands were energy-minimized, and the proteins were prepared by removing water molecules and adding hydrogen atoms. Docking simulations ran under default settings, comparing binding affinities via various scoring functions. Results: GOLD identified N-(CH3)3 and 3-NO₂ derivatives as strong binders, while Glide favored Erlotinib due to π-π stacking interactions. MOE highlighted Ethyl Vanillin and N-(CH3)3 derivatives, particularly for their polar interactions. The docking results demonstrated that each program had strengths depending on the ligand's interaction type. Conclusion: GOLD and MOE showed the most promise in identifying high-affinity binders for quinazoline derivatives targeting EGFR, while Glide excelled in handling hydrophobic interactions. The findings highlight the importance of selecting the appropriate docking tool based on ligand characteristics to optimize the drug discovery process for EGFR inhibitors.
Institutions: Mustansiriyah University, University of Jordan
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7117743374