Probing molecular docking problem by an improved quantum-behaved particle swarm optimization algorithm

The main objective of molecular docking is to find a model of interaction between a protein and ligand with a minimum binding energy. This process is driven by intricate algorithms and scoring functions. This paper mainly concentrates on the search algorithm used for solving the docking problem. Here, a new approach is proposed for the molecular docking problem that utilizes a hybrid algorithm that combines an improved quantum-behaved particle swarm optimization algorithm (QPSO) and the Solis and Wets algorithm. The improved QPSO algorithm that is based on individual particle evolutionary processes is known as individual particle evolutionary particle swarm optimization (IEQPSO). The IEQPSO algorithm was tested and compared with particle swarm optimization, QPSO, and its variants with a suite of benchmark functions. The results indicated the superiority of the proposed approach according to benchmark test functions. Then, the hybrid algorithm based on the IEQPSO algorithm was used for optimizing the energy function of the molecular docking problem and was compared with the classical Lamarckian genetic algorithm used by molecular docking software. Molecular docking and molecular dynamics simulation experiments revealed the effectiveness and feasibility of the proposed algorithm in solving the molecular docking problem.