Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
Molecular docking with multi-objective Particle Swarm Optimization
Applied Soft Computing
Bee-Inspired Protocol Engineering: From Nature to Networks
Bee-Inspired Protocol Engineering: From Nature to Networks
A survey: algorithms simulating bee swarm intelligence
Artificial Intelligence Review
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
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The identification of protein binding sites and the prediction of protein-ligand complexes play a key role in the pharmaceutical drug design process and many domains of life sciences. Computational approaches for protein-ligand docking (or molecular docking) have received increased attention over the last years as they allow inexpensive and fast prediction of protein-ligand complexes. Here we introduce the principle of Bee Nest-Site Selection Optimisation (BNSO), which solves optimisation problems using a novel scheme inspired by the nest-site selection behaviour found in honeybees. Moreover, the first BNSO algorithm -- Bee-Nest -- is proposed and applied to molecular docking. The performance of Bee-Nest is tested on 173 docking instances from the PDBbind core set and compared to the performance of three reference algorithms. The results show that Bee-Nest could find ligand poses with very small energy levels. Interestingly, the reference Particle Swarm Optimization (PSO) produces results that are qualitatively closer to wet-lab experimentally derived complexes but have higher energy levels than the results found by Bee-Nest. Our results highlight the superior performance of Bee-Nest in semi-local optimization for the molecular docking problem and suggests Bee-Nest's usefulness in a hybrid strategy.