Entropy‐based lamarckian quantum‐behaved particle swarm optimization for flexible ligand docking

Qi You, Chao Li, Jun Sun, Vasile Palade, Feng Pan

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Article number2200080
Pages (from-to)(In-Press)
JournalMolecular Informatics
Issue number3
Early online date31 Jan 2023
Publication statusPublished - Mar 2023


  • Organic Chemistry
  • Computer Science Applications
  • Drug Discovery
  • Molecular Medicine
  • Structural Biology


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