dc.creator | García Nieto, José Manuel | es |
dc.creator | López Camacho, Esteban | es |
dc.creator | García Godoy, María Jesús | es |
dc.creator | Nebro, Antonio J. | es |
dc.creator | Aldana Montes, José F. | es |
dc.date.accessioned | 2021-05-11T07:14:43Z | |
dc.date.available | 2021-05-11T07:14:43Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | García Nieto, J.M., López Camacho, E., García Godoy, M.J., Nebro, A.J. y Aldana Montes, J.F. (2019). Multi-objective ligand-protein docking with particle swarm optimizers. Swarm and Evolutionary Computation, 44 (February 2019), 439-452. | |
dc.identifier.issn | 2210-6502 | es |
dc.identifier.uri | https://hdl.handle.net/11441/108832 | |
dc.description.abstract | In the last years, particle swarm optimizers have emerged as prominent search methods to solve the molecular docking problem. A new approach to address this problem consists in a multi-objective formulation, minimizing the intermolecular energy and the Root Mean Square Deviation (RMSD) between the atom coordinates of the co-crystallized and the predicted ligand conformations. In this paper, we analyze the performance of a set of multi-objective particle swarm optimization variants based on different archiving and leader selection strategies, in the scope of molecular docking. The conducted experiments involve a large set of 75 molecular instances from the Protein Data Bank database (PDB) characterized by different sizes of HIV-protease inhibitors. The main motivation is to provide molecular biologists with unbiased conclusions concerning which algorithmic variant should be used in drug discovery. Our study confirms that the multi-objective particle swarm algorithms SMPSOhv and MPSO/D show the best overall performance. An analysis of the resulting molecular ligand conformations, in terms of binding site and molecular interactions, is also performed to validate the solutions found, from a biological point of view. | es |
dc.description.sponsorship | Ministerio de Ciencia e Innovación TIN2017-86049-R | es |
dc.description.sponsorship | Ministerio de Ciencia e Innovación TIN2014- 58304 | es |
dc.description.sponsorship | Junta de Andalucía P12-TIC-1519 | es |
dc.format | application/pdf | es |
dc.format.extent | 14 | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Swarm and Evolutionary Computation, 44 (February 2019), 439-452. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Multi-objective optimization | es |
dc.subject | Particle Swarm Optimization | es |
dc.subject | Molecular Docking | es |
dc.subject | Archiving Strategies | es |
dc.subject | Algorithm Comparison | es |
dc.title | Multi-objective ligand-protein docking with particle swarm optimizers | es |
dc.type | info:eu-repo/semantics/article | es |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial | es |
dc.relation.projectID | TIN2017-86049-R | es |
dc.relation.projectID | TIN2014- 58304 | es |
dc.relation.projectID | P12-TIC-1519 | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S2210650217304467 | es |
dc.identifier.doi | 10.1016/j.swevo.2018.05.007 | es |
dc.journaltitle | Swarm and Evolutionary Computation | es |
dc.publication.volumen | 44 | es |
dc.publication.issue | February 2019 | es |
dc.publication.initialPage | 439 | es |
dc.publication.endPage | 452 | es |
dc.contributor.funder | Ministerio de Ciencia e Innovación (MICIN). España | es |
dc.contributor.funder | Junta de Andalucía | es |