Employing Improved GA to Promote Molecular Docking Efficiency for Drug Design

This paper present a novel improved genetic algorithms (GA) to further the efficiency of molecular docking for drug design. According to our previous researches, docking is the crucial component of drug development. The number of docking sites affects the drug docking speed. Reducing the scope of the geometry search is the key task. This paper compares four geometry search methods as follows: Monte Carlo, simulated annealing, and genetic algorithms and improved GA, and refer to [1][2] in geometry search methods were compared when searching using a grid-based methodology in docking five HIV-1 protease-ligand complexes with known three-dimensional structures. The improved GA is better in terms of processing the search operation of geometry graphics. Finally, the demonstrated in simulation 1 that improved GA was utilized to sieve out the more approach global energy minimum from the raw and plenty docking sites.