GWOVina

GWOVina applies a grey wolf optimizer combined with AutoDock Vina to perform protein–ligand docking for predicting ligand binding poses on rigid and flexible receptors.

Key Features:

• Grey Wolf Optimizer (GWO): Incorporates the grey wolf optimizer (GWO) for global search to explore complex solution spaces during docking.
• Random Walk Strategy: Employs a random walk strategy to further enhance exploration during the optimization process.
• Dunbrack Rotamer Library: Uses the Dunbrack rotamer library for backbone-independent side-chain sampling in flexible-receptor docking.
• Integration with AutoDock Vina: Integrates with AutoDock Vina to leverage its docking scoring and pose evaluation capabilities.
• Rigid and Flexible Receptor Docking: Supports both rigid and flexible receptor docking, including explicit side-chain flexibility modeling.
• Performance and Speed: In rigid docking, achieves similar ligand pose root mean square deviation (RMSD), success rate, and affinity prediction accuracy to Vina; in flexible docking, attains higher success rates than Vina and AutoDockFR and runs approximately 2–7× faster than Vina and 40–100× faster than AutoDockFR.

Scientific Applications:

• Flexible-receptor docking: Addresses complex cases involving receptor side-chain flexibility to improve pose prediction.
• Virtual screening: Enables screening of compound libraries by predicting ligand binding poses across target receptors.
• Drug discovery and development: Facilitates identification and evaluation of potential drug candidates through efficient docking and pose assessment.

Methodology:

Hybrid computational approach integrating the grey wolf optimizer (GWO) for global search with AutoDock Vina's docking and scoring, employing a random walk strategy for enhanced exploration and the Dunbrack rotamer library for side-chain sampling in flexible-receptor docking, validated across four independent datasets.