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

Abstract

Protein-ligand docking is of great importance to drug design, since it can predict the binding affinity between ligand and protein, and guide the synthesis direction of the lead compounds. Over the past few decades, various docking programs have been developed, some of them employing novel optimization algorithms. However, most of those methods cannot simultaneously achieve both good efficiency and accuracy. Therefore, it is worthwhile to pour the efforts into the development of a docking program with fast speed and high quality of the solutions obtained. The research presented in this paper, based on the docking scheme of Vina, developed a novel docking program called RDPSOVina. The RDPSOVina employes a novel search algorithm but the same scoring function of Vina. It utilizes the random drift particle swarm optimization (RDPSO) algorithm as the global search algorithm, implements the local search with small probability, and applies Markov chain mutation to the particles' personal best positions in order to harvest more potential-candidates. To prove the outstanding docking performance in RDPSOVina, we performed the re-docking experiments on two PDBbind datasets and cross-docking experiments on the Sutherland-crossdock-set, respectively. The RDPSOVina exhibited superior protein-ligand docking accuracy and better cross-docking prediction with higher operation efficiency than most of the compared methods. It is available at https://github.com/li-jin-xing/RDPSOVina .

Keywords: AutoDock Vina; Protein–ligand docking; Random drift particle swarm optimization.