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( 2018b) METADOCK: a parallel metaheuristic schema for virtual screening methods.
Imbernón B Cecilia JM Pérez-Sánchez HE, et al. In: Proceedings of the 47th International Conference on Parallel Processing Companion, Eugene, OR, USA, 13–16 August 2018, p. ( 2018a) Designing HYPERDOCK: a parallel hyperheuristic method for virtual screening. Imbernón B Cecilia JM Cutillas-Lozano JM, et al. Journal of Artificial Intelligence Research 36: 267– 306. ( 2009) ParamILS: an automatic algorithm configuration framework. Hutter F Hoos HH Leyton-Brown K, et al. Journal of Medicinal Chemistry 47( 7): 1739– 1749. ( 2004) Glide: a new approach for rapid, accurate docking and scoring: method and assessment of docking accuracy. Friesner RA Banks JL Murphy RB, et al. Journal of Computer-Aided Molecular Design 15( 5): 411– 428. ( 2001) DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases. Ewing TJA Makino S Skillman AG, et al. (eds) Proceedings of the Computational Collective Intelligence-Seventh International Conference, ICCCI 2015, Part II, Madrid, Spain, 21–23 September 2015, pp. ( 2015) Parallel flexible molecular docking in computational chemistry on high performance computing clusters. Dolezal R Ramalho TC Franca TCC, et al. Google Scholarĭirectory of Useful Decoy (DUD) ( 2006) Directory of Useful Decoys. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2015, Madrid, Spain, 11–15 July 2015, pp. Cutillas-Lozano J Giménez D Almeida F ( 2015) Hyperheuristics based on parametrized metaheuristic schemes. ( 2011) Hybrid metaheuristics in combinatorial optimization: a survey. IEEE Transactions on Systems, Man, and Cybernetics: Systems 43( 3): 570– 586. ( 2013) Parameterized schemes of metaheuristics: basic ideas and applications with genetic algorithms, scatter search, and GRASP. Almeida F Giménez D López-Espn J, et al. The Journal of Supercomputing 58( 3): 292– 301. Almeida F Giménez D López-Espn J ( 2011) A parameterized shared-memory scheme for parameterized metaheuristics. The efficient exploitation of these systems enables HYPERDOCK to improve ligand–receptor binding. The different levels of parallelism can be used to exploit the parallelism offered by computational systems composed of multicore CPU + multi-GPUs. HYPERDOCK exploits the parallelism of METADOCK and includes parallelism at its own level. Multiple metaheuristics are explored, so the process is computationally demanding. 
HYPERDOCK represents a step forward the exploration for satisfactory metaheuristics is systematically approached by means of hyperheuristics working on top of the metaheuristic schema of METADOCK. METADOCK is a tool for the application of metaheuristics to VS in heterogeneous clusters of computers based on central processing unit (CPU) and graphics processing unit (GPU). The computational requirements of VS, along with the size of the databases, propitiate the use of high-performance computing. Virtual screening (VS) methods aid clinical research by predicting the interaction of ligands with pharmacological targets.
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