Entropy‐based lamarckian quantum‐behaved particle swarm optimization for flexible ligand docking. Issue 3 (31st January 2023)

• Record Type: Journal Article
• Authors: You, Qi; Li, Chao; Sun, Jun; Palade, Vasile; Pan, Feng
• Is Part Of: Molecular informatics. Volume 42:Issue 3(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-01-31
• Subjects: AutoDock -- flexible ligand docking -- local search -- quantum-behaved particle swarm optimization -- update strategy; Cheminformatics -- Periodicals; QSAR (Biochemistry) -- Periodicals; Structure-activity relationships (Biochemistry) -- Periodicals; Drugs -- Structure-activity relationships -- Periodicals; 615.19
• DOI: 10.1002/minf.202200080
• Languages: English
• ISSNs: 1868-1743

Abstract

Abstract: AutoDock is a widely used software for flexible ligand docking problems since it is open source and easy to be implemented. In this paper, a novel hybrid algorithm is proposed and applied in the docking environment of AutoDock version 4.2.6 in order to enhance the accuracy and the efficiency for dockings with flexible ligands. This search algorithm, called entropy‐based Lamarckian quantum‐behaved particle swarm optimization (ELQPSO), is a combination of the QPSO with an entropy‐based update strategy and the Solis and Wet local search (SWLS) method. By using the PDBbind core set v.2016, the ELQPSO is compared with the Lamarckian genetic algorithm (LGA), Lamarckian particle swarm optimization (LPSO) and Lamarckian QPSO (LQPSO). The experimental results reveal that the corresponding docking program of ELQPSO, named as EQDOCK in this paper, has a competitive performance in dealing with the protein‐ligand docking problems. Moreover, for the test cases with different number of torsions, the EQDOCK outperforms the other three docking programs in finding docking conformations with small root mean squared deviation (RMSD) values in most cases. In particular, it has an advantage of solving highly flexible ligand docking problems over the others. Abstract :