A Self-adaptive Local Search Coordination in Multimeme Memetic Algorithm for Molecular Docking

Abstract

Molecular Docking is a methodology that deals with the problem of predicting the non-covalent binding of a receptor and a ligand at an atomic level to form a stable complex. Because the search space of possible conformations is vast, molecular docking is classified in computational complexity theory as a NP-hard problem. Because of the high complexity, exact methods are not efficient and several metaheuristics have been proposed. However, these methods are very dependent on parameter settings and search mechanism definitions, which requires approaches able to self-adapt these configurations along the optimization process. We proposed and developed a novel self-adaptive coordination of local search operators in a Multimeme Memetic Algorithm. The approach is based on the Biased Random Key Genetic Algorithm enhanced with four local search algorithms. The self-adaptation of methods and radius perturbation in local improvements works under a proposed probability function, which measures their performance to best guide the search process. The methods have been tested on a test set based on HIV-protease and compared to existing tools. Statistical test performed on the results shows that this approach reaches better results than a non-adaptive algorithm and is competitive with traditional methods.