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

摘要

Molecular Docking is a methodology that deals with the problem of predicting the non-covalent binding of a receptor and a lig-and 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 metaheuris-tics 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.