Journal of Global Optimization
Distance Geometry Optimization for Protein Structures
Journal of Global Optimization
Multi-objective optimisation of the protein-ligand docking problem in drug discovery
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Molecular docking with multi-objective Particle Swarm Optimization
Applied Soft Computing
Toward a Robust Search Method for the Protein-Drug Docking Problem
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
PLANTS: application of ant colony optimization to structure-based drug design
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
Opposition-Based Differential Evolution
IEEE Transactions on Evolutionary Computation
Survey A review of opposition-based learning from 2005 to 2012
Engineering Applications of Artificial Intelligence
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Computer simulation of binding a small molecule (ligand) to the protein receptor is one of the most important issues in present drug design research. The goal of this procedure is to find the best protein-ligand complex by in silico methods. Among different types of approaches that have been developed, metaheuristic algorithms have a major contribution to solve docking problem. In this paper, a population based iterative search algorithm is used for finding the best docking pose. This algorithm is an extension of the differential evolution (DE) algorithm called opposition-based differential evolution (ODE). Also ODE is enhanced by a local search algorithm and a pseudo-elitism operator. The scoring function which is used in this paper is the AutoDock scoring function. Six different protein-ligand complexes are used to verify the efficiency of the proposed algorithm. The experimental results show that the modified ODE (mODE) is more robust and reliable than the other algorithms such as simulated annealing and Lamarckian genetic algorithm.