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  • Title: Random drift particle swarm optimisation algorithm for highly flexible protein-ligand docking.
    Author: Fu Y, Chen Z, Sun J.
    Journal: J Theor Biol; 2018 Nov 14; 457():180-189. PubMed ID: 30170044.
    Abstract:
    Molecular docking has emerged as an important tool in drug design and development. Currently, there is a relatively large and ever-increasing number of molecular docking programs. However, despite the great advances in the docking technique over the last decade, most methods cannot be used to dock highly flexible ligands successfully. In this study, based on the Autodock software, a new search algorithm, hybrid algorithm of Random Drift Particle Swarm Optimisation and local search (LRDPSO), that focuses on protein-ligand applications was presented. In our approach, we considered the ligand flexibility and strategies that aimed to improve binding affinity prediction in the context of a docking-based investigation. The experimental results revealed that our approach led to a substantially lower docking energy and higher docking precision in comparison to the LGA, PSO and QPSO algorithms. The LRDPSO algorithm was able to identify the correct binding mode of 83.6% of the complexes. In comparison, the accuracy of QPSO, PSO and LGA is 73.1%, 68.7% and 68.7%, respectively. For LRDPSO docking, satisfactory docking results can be obtained when relatively big ligands with many rotatable bonds are docked against protein binding pockets in which flexibility does play an important role. Thus, the novel LRDPSO algorithm predictions for highly flexible ligands are more reliable, and would increase the predictive power and widen the applications of this important computational tool.
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