SSGARL: Hybrid evolutionary computation and reinforcement learning for flexible ligand docking

This paper presents and investigates the performance of a hybrid algorithm of steady-state genetic algorithm and reinforcement learning (SSGARL) in the problem of protein-ligand docking. The performance was measured in terms of the lowest found docking energy, the number of energy evaluation and the time taken to complete a docking task. Ten ligands of varying flexibility were chosen to bind with thermolysin to compare the performance of SSGARL and Iterated Local Search global optimizer´s algorithm of AutoDock Vina. The results reveal that SSGARL finds the lowest docking energy, requires lesser number of energy evaluation and is faster in docking the highly flexible ligands.