Enhanced-Hybrid-Age Layered Population Structure (E-Hybrid-ALPS): A Genetic Algorithm with Adaptive Crossover for Molecular Docking Studies of Drug Discovery Process

Abstract

Objectives: Age Layered Population Structure (ALPS) which introduces time labels into a traditional Genetic Algorithm (GA) is a novel search metaheuristic in overcoming premature convergence There are two models of ALPS namely generational and steady-state with their own merits and demerits Present work has been taken up to devise a search algorithm E-Hybrid-ALPS with the combined concepts and advantages of both the models Methodology: E-Hybrid-ALPS not only combined the concepts and advantages of both the models but also considered weak individual solutions to the mating pool and adaptively applied the crossover operator A search algorithm, a component of the molecular docking tool plays a vital role in the success of molecular docking used in drug discovery Hence, E-Hybrid-ALPS has been implemented as a search algorithm for molecular docking The execution was carried out with two receptor-ligand combinations namely receptor CYP2C8 and ligand Chloroquine, a therapeutic option in the treatment of Corona Virus Disease (COVID-19) and also a drug used in the treatment of Malaria and receptor CYP2B6 and ligand Cyclophosphamide a drug used in the treatment of cancer Results: E-Hybrid-ALPS generates poses of the ligand in the active site of the receptor, calculates the binding energy of each pose and outputs the pose with the lowest binding energy The performance was evaluated by comparing it with the widely used molecular docking tools AutoDock and AutoDockVina which employ Lamarckian GA as a search algorithm Lowest binding energy found by E-Hybrid-ALPS was significantly low as compared to the lowest binding energy found by AutoDock and AutoDockVina Conclusion: E-Hybrid-ALPS which generates a ligand/drug pose with the lowest binding energy can be implemented as a search algorithm for AutoDock molecular docking tool This helps the drug discoverer in designing a drug with a better binding affinity as lower binding energies indicate higher binding affinity © IJCRR