A molecular docking algorithm method based on quantum particle swarm optimization algorithm

Abstract

The invention relates to the technical field of computer application, in particular to a molecular docking calculation method based on a quantum particle swarm optimization algorithm, wherein, the molecular docking calculation method based on the quantum particle swarm optimization algorithm comprises the following steps: obtaining a protein PDB file; preprocessing the protein PDB file to obtain aprotein receptor file and a protein ligand file; performing lattice docking pretreatment on the protein receptor file and the protein ligand file; setting lattice docking parameters and selecting docking algorithm to run molecular docking program for molecular docking; outputting docking results. The molecular docking calculation method based on the quantum particle swarm optimization algorithm provided by the invention has the advantages of simple steps, few control parameters and easy solution.

Description

technical field The invention relates to the field of computer application technology, in particular to a molecular docking calculation method based on quantum particle swarm optimization algorithm. Background technique The design and development of drugs has always been characterized by high investment, high risk, and long cycle, which seriously restrict the development of the pharmaceutical field. The reason why new drug research is progressing slowly is that in the initial stage of new drug research, there is no effective means to screen drug molecules, and it takes a lot of manpower, material and financial resources, but the results cannot be guaranteed. Therefore, it is very necessary to establish an effective drug molecular screening method. Computer drug molecular design is a virtual screening method that combines traditional drug design theory with numerical optimization methods. The introduction of computer technology has greatly reduced the design time of drug mol...

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Problems solved by technology

At the same time, how to improve the shortcoming that the intelligent optimization algorithm is prone to fall into local optimum during the molecular docking optimization process, so as to make the optimization results more accurate and the docking accuracy higher is a problem that those skilled in the art need to solve

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