Bonding as a swarm (original) (raw)

2011, Proceedings of the 13th annual conference on Genetic and evolutionary computation - GECCO '11

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 -called Bee-Nest -is proposed and is applied to the protein-docking problem. 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 good energy levels. Interestingly, the reference Particle Swarm Optimization (PSO) produces results that are qualitatively closer to wet-lab experimentally derived complexes but of worse energy than Bee-Nest. Our results clearly highlight the superior performance of Bee-Nest in semi-local (regional) optimization for the molecular docking problem, and suggests its usefulness in a hybrid strategy.

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