RDPSOVina: the random drift particle swarm optimization for protein-ligand docking.

Area of Science:

• Computational chemistry
• Drug discovery
• Bioinformatics

Background:

• Protein-ligand docking is crucial for predicting binding affinity and guiding drug development.
• Existing docking programs often struggle to balance speed and accuracy.
• Developing efficient and accurate docking tools remains a significant challenge.

Purpose of the Study:

• To develop a novel protein-ligand docking program, RDPSOVina, that achieves high accuracy and efficiency.
• To introduce a new search algorithm to improve the performance of docking simulations.
• To provide a valuable tool for drug design and lead compound optimization.

Main Methods:

• RDPSOVina is based on the Vina docking scheme but incorporates a novel search algorithm.
• It utilizes the random drift particle swarm optimization (RDPSO) algorithm for global search.
• Local search with low probability and Markov chain mutation are applied to enhance candidate exploration.

Main Results:

• RDPSOVina demonstrated superior accuracy in re-docking experiments on PDBbind datasets.
• The program showed improved cross-docking prediction accuracy on the Sutherland-crossdock-set.
• RDPSOVina achieved higher operational efficiency compared to most other docking methods.

Conclusions:

• RDPSOVina offers a significant advancement in protein-ligand docking, providing both speed and accuracy.
• The novel RDPSO algorithm and search strategy contribute to its enhanced performance.
• This tool can accelerate drug discovery by improving the prediction of lead compound binding.