PSOVina: The hybrid particle swarm optimization algorithm for protein-ligand docking.

Area of Science:

• Computational chemistry
• Drug discovery
• Bioinformatics

Background:

• Protein-ligand docking is crucial for identifying drug candidates.
• Accurate prediction of ligand pose and orientation in protein binding sites remains a challenge.
• Efficient conformational search is vital for large-scale docking applications.

Purpose of the Study:

• To develop a novel computational method, PSOVina, for enhanced protein-ligand docking.
• To improve the efficiency of molecular docking by combining swarm intelligence with established local search algorithms.
• To evaluate PSOVina's performance against AutoDock Vina in terms of speed and accuracy.

Main Methods:

• Integration of the particle swarm optimization (PSO) algorithm with the Broyden-Fletcher-Goldfarb-Shannon (BFGS) local search method from AutoDock Vina.
• Validation using a dataset of 201 protein-ligand complexes from the PDBbind database.
• Testing on virtual screening datasets (DUD) with representative targets and decoys.

Main Results:

• PSOVina demonstrated a significant reduction in execution time, ranging from 51% to 60%.
• Prediction accuracy in docking and virtual screening experiments was maintained compared to the original Vina program.
• The enhanced efficiency positions PSOVina as a valuable tool for large-scale drug discovery efforts.

Conclusions:

• PSOVina offers a substantial improvement in computational efficiency for protein-ligand docking.
• The hybrid approach provides a robust and faster alternative for virtual screening and drug design.
• This work paves the way for swarm-based algorithms in future molecular docking software.