HYPERDOCK: Improving virtual screening through parallel hyperheuristics
Virtual screening (VS) methods aid clinical research by predicting the interaction of ligands with pharmacological targets. The computational requirements of VS, along with the size of the databases, propitiate the use of high performance computing. METADOCK is a tool for the application of metaheur...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2019 |
| País: | España |
| Institución: | Universidad Católica San Antonio de Murcia (UCAM) |
| Repositorio: | RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murcia |
| OAI Identifier: | oai:repositorio.ucam.edu:10952/8811 |
| Acceso en línea: | http://hdl.handle.net/10952/8811 |
| Access Level: | acceso abierto |
| Palabra clave: | Virtual screening Molecular docking HPC Metaheuristics Hyperheuristics |
| Sumario: | Virtual screening (VS) methods aid clinical research by predicting the interaction of ligands with pharmacological targets. The computational requirements of VS, along with the size of the databases, propitiate the use of high performance computing. 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). 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. Multiple metaheuristics are explored, so the process is computationally demanding. HYPERDOCK exploits the parallelism of METADOCK and includes parallelism at its own level. The different levels of parallelism can be used to exploit the parallelism offered by computational systems composed of multicore CPU þ multi-GPUs. The efficient exploitation of these systems enables HYPERDOCK to improve ligand–receptor binding. |
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